Taming the Long Tail in Human Mobility Prediction
Authors: Xiaohang Xu
Renhe Jiang
Chuang Yang
zipei fan
Kaoru Sezaki
Venue: NeurIPS 2024 poster (2024)
Keywords: Human Mobility
Next POI Prediction
Long-Tail Learning
Primary area: machine_learning_for_social_sciences
TL;DR: A human mobility prediction model for long-tail distribution issue

Abstract: With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next POI (point-of-interest) visit using their past visit history. However, the uneven distribution of visitations over time and space, namely the long-tail problem in spatial distribution, makes it difficult for AI models to predict those POIs that are less visited by humans. In light of this issue, we propose the $\underline{\bf{Lo}}$ng-$\underline{\bf{T}}$ail Adjusted $\underline{\bf{Next}}$ POI Prediction (LoTNext) framework for mobility prediction, combining a Long-Tailed Graph Adjustment module to reduce the impact of the long-tailed nodes in the user-POI interaction graph and a novel Long-Tailed Loss Adjustment module to adjust loss by logit score and sample weight adjustment strategy. Also, we employ the auxiliary prediction task to enhance generalization and accuracy. Our experiments with two real-world trajectory datasets demonstrate that LoTNext significantly surpasses existing state-of-the-art works.

Taming the Long Tail in Human Mobility Prediction
Xiaohang Xu', Renhe Jiang', Chuang Yang', Zipei Fan', Kaoru Sezaki!
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Abstract
With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next POI (point-of-interes)) visit using their past visit history. However, the uneven distribution of visitations over time and space, namely the long-tail problem in spatial distribution, makes it difficult for Al models to predict those POIs that are less visited by humans. light of this issue, we propose the Long-Tap Taig Adjusted Next POI Prediction (LoTNext) framework for mobility prediction, combining Long-Tailed Graph Adjustment module to reduce the impact of the nodes in the user-POI interaction graph and novel Long-Tailed Loss Adjustment module to adjust loss by logit score and sample weight adjustment strategy. Also, we employ the auxiliary prediction task to enhance generalization and accuracy. Our experiments with two real-world trajectory datasets demonstrate that LoTNext significantly surpasses existing state-of-the-ar works.
Introduction
Human mobility prediction is essential in various applications, aiming to forecast the next Point of Interest (POI) a user may visit based on their historical location data, preferences, and patterns [10] 8. 53 33 By predicting user movements, it supports urban planning, traffic management, and environmental protection, and provides intelligent personalized Location-Based Social Networking (LBSN) services
37), enhancing people's 's life quality.
The growth of POI prediction tasks is closely linked to the development of LBSN platforms, where users frequently share their itineraries and reviews, leading substantial accumulation of geograph- ical visitation data. However, data collection faces challenges due to network and privacy constraints on mobile devices and the requirement for user authorization to record check-ins. This often results in data being sparse and biased towards popular locations, exhibiting severe long-tail effect. Currently, these methods fall into two primary categories: Sequence-based and Graph-based models.
Sequence-based models treat users' trajectories independent visitation sequences. Existing methods include Recurrent Neural Networks (RNNs) 6
11,, Long Short Term Memory (LSTM) 2 20 14 [3] and Gated Recurrent Unit (GRU) 4 for modeling the rich spatial-
12
temporal information implied in the visitation sequence.
Graph-based models focus on building models and data structures to capture trend information in the data to enhance the prediction performance, such the movement trends among all users 45
39 34 35 41). geographic adjacency [27] 22 26], and category transition between POIs [49 48)
This helps in modeling complex global visitation preferences and the semantic context of locations.
"Corresponding author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Nevertheless, existing works often overlook the intrinsic long-tailed distribution problem in spatial visitation patterns. As shown in Figure II it provides the evidence of long-tailed distribution on Gowalld] dataset, a real LBSN dataset. From the visualization results, it is evident that only a few POIs are visited more than 100 times. addition, the illustrative diagram above the graph presents a hypothetical scenario. The prediction model might inaccurately predict that user would visit a common location, such as McDonald's (a Head POI), while the user actually visits a less common place like ramen restaurant (a Long- Tail POI). This highlights the importance of designing models capable of accurately predicting visits to Long-Tail POIs. The concept of a long-tailed distribution, while first extensively studied and addressed within the computer vision (CV) field [4], manifests differently in the context of POI prediction. In POI prediction, long-tailed POIs are embedded within users' complex trajectories. This distinction means that unlike in CV, where long-taild long-tailed samples can be selectively augmented to balance datasets, selecting long-tailed POIs without considering the spatial-temporal context in which they occur risks losing crucial trajectory information.
Against this background, to mitigate the long- tail problem in the human next POI prediction task, we propose the Long- -ail Adjusted Next POI prediction (LoT framework, which
3000
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is a generie framework aimed at optimizing and fully utilizing long-tailed POI information.
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Long-Tailed Graph Adjustment module to
duce noise and long-tailed nodes in the user- POI interaction graph, thereby mitigating the im- pact of long-tailed POIs on model performance. Through graph adjustment, the model can more accurately capture spatial-temporal information from trajectory contexts. Furthermore, to pre- vent the model from overly focusing on head POIs freguerey POIs), we propose the Long-Tailed Tailed Loss Adjustment module to balance the loss between head and tail POI data. Finally, to alleviate the intrinsic sparsity issue without introducing additional data sources, we incorporate auxiliary prediction tasks to further integrate the POI feature and spatial-temporee information.
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Figure 1: The long-tailed distribution for POI check-in frequency from the Gowalla dataset.
We conclude our contributions as follows: (1) We propose the LoTNext framework based on graph adjustment to effectively address the challenges of dataset-inherent sparsity in the user-POI interaction graph. (2) We design the Long-Tailed Loss Adjustment module for adaptive sample re-weighting, which more effectively balances the loss between head and tail samples.
(3) We introduce the auxiliary prediction task, which achieves complementarity of POI feature information and spatial-temporal information. (4) We evaluate LoTNext on two public LBSN datasets, comparing it with numerous baselines. The results demonstrate that LoTNext significantly outperforms fatee-ofthe--aee art methods.
2
Related Work
Next POI Prediction. Most current works on the next POI prediction treat trajectories as time series, further incorporating spatial-temporal contexts into models to enrich the semantics of POIs. The pioneering ST-RNN [18] introduces ppatial-tempora intervals to RNN for context awareness. DeepMove (6) integrates LSTM with attention mechanisms to consider both the short-term and long- term preferences of users comprehensively, and LSTPM (29 enhances spatial context integration. The Flashback model [3) tackles user sparsity by mining similar contexts in historical data. However, due to the limited capability of RNN in modeling long sequences, researchers have explored using graphs for improvements. GETNext 45. based the Transformer architecture, combines global mobility patterns graph with various spatial-temports contexts to fully utilize information among similar user trajectories for improving prediction performance. Graph-Flashback (27 considers constructing a knowledge graph to improve POI representation and integrates it with sequence recommendation models. SNPM 446 builds a POI similarity graph to aggregate similar POIs and enhance POI representation results. However, all these studies overlook the significant impact of the long-tail tail problem on the next POI prediction.
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Figure 2: The Architecture of Long- Tail Adjusted Network for Next POI Prediction (LoTNext).
Long- Tailed Learning. The long-tale tail problem has always been a focus in the fields of CV B 30
and recommendation systems [[S]. The most direct solution is re-sampling [50], varying from
random to progressively balanced. Another common strategy is logit adjustment [24 25 31]
aimed modifying the logistic output address the imbalanced data class problem. A study by Google [24] has proven that logit adjustment satisfies Fisher consistency and can effectively minimize the average error per category. Compared to the CV field, the long-tail problem is more pronounced in recommendation systems [9]. Typically, the number of items far exceeds the number of users, leading many items rarely or infrequently accessed by users. Some works tackle the long-tail problem by learning item similarities through random walk algorithms 47 or utilizing transfer learning transfer the knowledge from head tail data [5T]. 36 used meta-learning enhance the information representation of the user-item graph. [21] introduced novel edge addition module to enrich the connectivity for tail samples. However, unlike traditional recommendation tasks, the next POI prediction involves complex spatial-temporal -tempora semantics due to the nature of trajectory data, making it more challenging to improve the representation of long-tailed POI samples. To the best of our knowledge, our work is the first to propose general framework for the next POI prediction under the long-tat -til problem.
Problem Definition
Given user set U {ui, U2, Elnnn and a POI set P {PI, 1.p1,P,.00 PIp with [U] and |P] indicating the number of users and POIs respectively, we denote the POI check-in triplet (u, p, f). which means user u visits POI at time t. Each POI is a triplet (lat, lon, freq), representing its latitude, longitude, and visit frequency. We proceed to outline our problem definition follows.
Definition (User Next POI Prediction) Given user check-in sequence denoted (1),4)) (p2, ........ (Pn, bn)), our goal is to predict a list of top POIs that the user u is likely visit next, which can be taken as a typical sequence classification task over [P| POI candidates. In particular, our work focuses on how to accurately predict the "less visited" POIs belonging to the long-taiie tailed interval.
Qu=
Methodology
In this section, we introduce the details of the LoTNext framework, as shown in Figure 2 which consists of the preliminary POI prediction model, LoigeTailed Graph Adjustment module, and Long-Tailed Loss Adjustment module.
4.1 Preliminary Model
We first construct preliminary end-to-end model, which is designed for precise next POI prediction.

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Trajectory Embedding Layer. For the embedding generation, we initialize embeddings for POIs E' E pl,,,,, timestamps E" E [ and users E E Rl01xd_, where dp. de and dy are the corresponding embedding dimension and [T'] is the number of the time slots. our case, considering each hour slot over week, there are 168 time slots in total. During the sequence processing phase, we select embedding from E and Ed based on the POI and time indices in the input sequence Qu con-
struct an embedding sequence X (dx as X [(pp] Epg.... Ep, II,EE, E E[,)] where n is the sequence length and denotes the concatenation operation.
Transformer Encoder. Transformer [32 architecture in modeling trajectories has been demonstrated adopt its encoder block to encode spatial-temporal contexts within trajectories, focusing on capturing long-distance dependencies through multi-layer stacking. To
in multiple studies [39 45 40
incorporate learnable positional embedding Fopos mgd with the raw embedding sequence X to form the Transformer input X X + Poos. Next, we define the Transformer encoder block as follows:
maintain positional information in the sequence,
Z LayerNorm( X Multi-Head Attention (X)),
- LayerNorm( () + FFN(2)),
where FFN is a fully connected layer and the Multi-Head Attention can be described as:
Multi-Head X) [head] (..dd//l.-//hhaddd.
XX
head, Softmax
X X'WV
vdk
where X is the input of the i-th head, W W W and WV are the learnable weights matrix, h is the number of the head, and yde is the scaling factor.
we introduce the Spatial Contextual Attention Layer to analyze the relationship between spatial proximity and user interactions. assigns dynamic weights to POIs in sequence, taking into account both the order and geographical distances, focusing on POIs most influential to future movements. The spatial weight W/e for each POI ph in the sequence (P1, P2..... Pk..... Pr), considering its spatial distance to all previous POIs p1 r>p,, is:
Spatial Contextual Attention Layer. Inspired by Flashback 43
Wi
Σ j=1
(e (e-((s(es +
(3)
where A(pj. Pk) the haversine distance between Py and Pk- 8 the distance decay weight, and e is a small constant to prevent division by zero. Considering &μ, an element from the Transformer output
sequence Z (ā 22, ..., dR, .... ). The refined output z is obtained by applying spatial weight to the cumulative previous outputs, defined as follows:
Σ
2j=1 wj -.
2
Prediction Layer. To provide personalized predictive outcomes and ensure accurate representation even for users with fewer check-ins, we further introduce user embeddings E and fuse it with refindd output ž' to form the input O = | [ for the final fully connected layer L OW + bP. where W E +d., is the weight matrix of the fully connected layer, b E R is the bias, and E |Pl is the logit scores for n steps of POI prediction. As the POI prediction is essentially a sequence classification task, we adopt the standard cross-entropy loss Lce as follows:
N
exp(1)) IPl 1=1 exp(1)))
LCE
tt log
(5)
N
where 1/ IR. represents the logit score of the k-th sample for the i-th POI candidate in P. and of is the ground-truth indicator on POI label for the k-th sample. In our implementation, we mix the steps of prediction and B samples in one batch together N B samples in total.

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4.2 Long-Tailed Tailed Graph Adjustment
In the next POI prediction task, we model user-POI interactions via a User-POI Interaction Graph Gtv [RU pl)xd is the input node feature matrix of the GIv, d dg = du and A E P is the adjacent matrix. It's bipartite graph where user U and POI P nodes connect through edges symbolizing interaction frequencies or preferences. Graph Neural Networks (GNNs) [38] can leverage graphs learn complex node representations, but performance hinges on graph quality. However, the GdD often has long-tailed distributions--men limited to few nodes with high visit frequency. which affects the quality of node embeddings and model efficacy. To tackle the long-tail problem in G'n, we propose denoising layer to prune and reduce sparse interactions caused by the distribution. This layer evaluates edge importance, retaining only beneficial edges for learning. Initially, an attention layer weights edges according to user-POI embedding interactions, processed by multilayer perceptron (MLP) to obtain attention scores:
=(Vln, ()'*,,A'm) where Vin = [ [|F?*]
interactions are
A, = (W R. LE [E]]+ + 6²) + bD))
(6)
Here, a denotes the sigmoid function, ensuring that the attention scores Asj lie in the (0, 1) interval, EV and E means the embedding of user and POI, W represents the trainable weight matrix, and b represents the bias. Based on the attention score A.j. the denoising process applies thresholding operation to filter out edges with scores below a threshold 6, effectively reducing noise and focusing
on high-quality interactions. This process aims to derive the denoised graph Č ()*n A!)) can be formalized as:
Al = A 1[4,) 6).
where Af denotes the refined edge and [[-] is the indicator function. The threshold ó controls the sparsity the graph, only edges with weights signifying a strong user-POI relationship are retained in - It is worth noting that when all edges fall below 8, the edge with the highest attention score is retained to prevent isolated nodes in the graph. The model then leverages the Graph Convolutional Network (GCN) [16] layer to learn the node embedding E of the G"V, as follows:
Fin LeakyReI.U (ID 3 ( (1
(8)
where D/u is the degree matrix of the A'm and W is the graph convolution weight. It is noted that here we perform a slicing operation 'n' = [ :] to select the node embedding representing the POI of Ghv,. Beyond merely focusing on direct interactions between users and POIs, we further extend our exploration to utilize all users' check-in data to uncover global mobility patterns among POIs. We build a user-indepentee
directed Global Transition Graph G = =(V"=, """), where >T E pl and Tr e [| Here, DTr is equal to E", and A stores the visit frequency between two different POIs. It is important to note that we do not perform a denoising process on the GTv, as it accurately reflects the mobility patterns of all users, containing a wealth of global transition information. Similarly, we employ GCN refer Equation (8) to learn the node embedding T of the GTh,. Finally, we perform mean pooling to combine the two node embeddings E' and ET,, which yields the denoised POI embedding É" 1/2 (E" + ETTr) that incorporate comprehensive user mobility patterns from interaction and transition graphs. To introduce denoised embedding in our model, we refine our input embedding sequence X construction process EP EP (1(T, ET, as X = [5 pp
ET,)) ET.
4.3
Long-Tailed Loss Adjustment
Logit Score Adjustment. Traditional classification models often mechanically employ the softmax function for outputting predictions, which may lead to an oversight of the potential discrepancies in the posterior distributions between training and testing data. To improve model discrimination, logit adjustment has been explored, which originates in the domain of face recognition [28] 52), It
involves modifying the model's 's output layer (i.e., logits) to encourage the generation of more compact intra-class representations while increasing the distance between classes, thereby augmenting the model's capability to handle long-tailed data.
To address the long-tail problem in human next POI prediction tasks, we propose the Logit Score Adjustment module. It adjusts the logits by factor that is inversely correlated with the frequency
5

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of occurrence of each label, effectively dampening the influence of frequently occurring labels and amplifying that of rarer ones. The adjustment factor a, for label with frequency freq is given by:
04 1
log(freq; +
(rg(fregma: + c)
where freqmaz is the maximum label frequency observed in the dataset, is the logit adjustment weight and is small constant to stabilize the logarithm operation. We can adjust final logits 7, e R' based on the logits I I as I + as..
Sample Weight Adjustment. Based on the Equation (5). for the standard cross-entropy loss, we find due the nature of the softmax function, which normalizes the logits I into probabilities, the model can become biased toward head classes. This imbalance means that the model's updates predominantly driven by the head classes, the loss from incorrectly classified examples in long-tailed classes contributes insignificantly to the overall loss. Even marginal improvements in the predictions for these long-taile classes may contribute insignificantly to the overall loss. Therefore, it is necessary to reweight long-tailed samples, like with Focal Loss [17. which reduces the weights of well-classified samples to better focus on minority classes, but does not explicitly consider the imbalance degree between classes in the long-tailed distribution. Unlike Focal Loss, we propose a novel Long- Tail Adjusted (LTA) loss to adaptively re-weight long-tailen samples. Specifically, for the final prediction layer, we have the hidden inputs of N samples O = (o o². o?...., oN) and the weights W =ww", 2² .... Pl for Pl candidates, where of E (d. is from the k-th sample. The true class label for the k-th sample is denoted by yk- We can take wilh E (dd++dy) as the class "center" for the class which the k-th sample truly belongs. Then we assess the impact posed by the k-th
sample to the overall prediction through the cosine similarity between ot and w as follows:
cos(o whhh
(10)
1 W
Based these cosine similarities, we compute the adjusted vector magnitude şh for each sample as:
1. g* [
cos(o* w 0, cos(o* w cos(o w S 0.
(11)
Then determine the geometric mean of the vector magnitude serve baseline magnitude The traditional definition of the geometric mean of the vector magnitudes the N-th root of their product & = </c However, it can be problematic in practice due to numerical underflow or overflow when dealing with very small or very large values. To mitigate this issue, we utilize logarithm to turn the product into sum, making the calculation more numerically stable, as follows:
Š = exp
1
Σ
N
log(s*
+ te)).
(12)
N
k=1
We calculate adaptive weights & for each sample using the deviation of vector magnitude from the geometric mean:
{1.
E 0. - O.
1i+ck-E.
(13)
FFinally, the overall Loig-Tail Adjusted loss LITA can be formulated as:
1
N
exp(1
LITA
y log
(14)
N
IP exp(7))
By combining the Logit Score Adjustment and the Sample Weight Adjustment, we present nuanced approach to recalibrating the model's s focus across the spectrum of label frequencies. It ensures that each sample contributes to the model's learning process in proportion to significance, as dictated by the distributional characteristies of the datasct and the discriminative capacity of the model.

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4.4 Model Optimization
Building upon our Long-Tailes Loss Adjustment module, we further embrace auxiliary prediction tasks to optimize LoTNext. To incorporate these tasks, we define a joint loss function that combines three distinct loss components: the standard cross-entropy loss (LCr), the Long-Tail Adjusted Loss (LLTA), and the Mean Squared Error loss for auxiliary time prediction (Lz)) Each component serves a critical role: LCE ensures the fidelity of the next POI prediction, LLT addresses the long-tate long-tailed data imbalance through adaptive weighting, and LAuz measures the accuracy of the timing predictions, an auxiliary task that supports the model by providing it with temporal context, thereby improving prediction accuracy and robustness, which can be denoted as:
LAM
111
(15)
where 1* is the forecasted time slot of k-th candidate POI and j is the ground truth time slot. The overall loss function is constructed as weighted sum of these components, with the weights X being learnable parameters, as follows:
Joint d1 XILER A2LITA + AzC
(16)
Experiments
Datasets & Baselines. We evaluate our LoTNext on two publicly available real-world LBSN datasets: Gowalla and Foursquare user check-in record includes the User ID, POI ID, latitde, longitude, and timestamp. To focus solely the impact of long-tailed POIs and ensure the dataset's 'ss quality, filter out inactive users with fewer than 100 check-ins. We then split each user's check-in records according temporal order, using the first 80% for training and the remaining 20% for testing. To batch training, we uniformly segment the length of each input trajectory (e.g., 20). The specific statistical results are shown in Table IT where we additionally calculated the percentage of POIs with a frequency smaller than 200 times and smaller than 100 times out of the total number of POIs. For instance, defining long-taile POIs those with a frequency of less than 100 times, approximately 98.38% of POIs could be considered long-tailed tailed POIs. Considering both Table I and Table [2] the reason why the model performs about 20% points better on Foursquare compared to Gowalla is due to the more severe long-tail effect on the Gowalla dataset, along with a sparser density of the dataset.
Table 1: Basic dataset statistics.
Dataset
Gowalla
Foursquare
Duration
2009.02-2010.10
2012.04-2014.01
|Ussers
7.768
45,343
#POIs
106,994
68,879
#Check-ins
1,823,598
9,361,228
*Trajectories
84,357
429,071
Density
0.0022194
0.002997
POI frequency <200 (%)
99.57%
89.26%
POI frequency <100 (%)
98.38%
63.70%
To demonstrate the performance of the LoTNext, we implement the following 10 state-of-the-aut methods as the comparison baselines:
ST-RNN 18
extends the RNN by introducing the spatial and temporal transition matrices.
Deep considers long-term and short-term interests of users by attention mechanism.
LBSN2 42 introduces the hypergraph and calculates the similarity of users and time embed-
dings to rank POIs.
LightGCN 9 simplifies the structure of Graph Convolutional Network (GCN) to learn user preferences for POIs.
7

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Table 2: Ace@k and MRR performance comparison Gowalla and Foursquare datasets.
Gowalla
Foursquare
Model
Acc
Acc@5
Acc@
MRR
Acc@1
Acc@5
Acc@
MRR
ST-RNN
18
0.0900
0.2120
0.2730
0.1508
0.2290
0.4310
0.5050
0.3248
DeepMove
0.0625
0.1304
0.1594
0.0982
0.2400
0.4319
0.4742
0,3270
LBSN2Vec
42
0.0864
0.1186
0.1390
0.1032
0.2190
0.3955
0.4621
0,2781
LightGCN
9
0.0428
0.1439
0.2115
0.1224
0.0540
0.1790
0.2710
0.1574
LSTPM 29
0.0721
0.1843
0.2327
0.1306
0.2484
0.4489
0.5018
0,3365
Flashhack 43
0.1158
0.2754
0,3479
0.1925
0,2496
0.5399
0.6326
0,3805
STAN 23
0.0891
0.2096
0,2763
0.1523
0.2265
0,4515
0,5310
0.3420
GETNext 45
0.1419
0.3270
0,4081
0.2294
0.2646
0,5640
0.6431
0.3988
Graph-Flashbact
27
0.1495
0.3399
0,4242
0.2401
0.2786
0.5733
0.6501
0.4109
SNPM 46
0.1593
0.3514
0.4346
0.2505
0.2899
0.5967
0.6763
0.4278
NoxN (Ours)
0.1668
0.3605
0.4429
0.2591
0.3155
0.6059
0,6812
0,4469
LSTPM 29 proposes geo-nonlocal LSTM to further extend DeepMove structure.
searches the most similar hidden states in historical information based on the current context information and updates the model.
Flashback +
STAN 23
explores the influence between non-adjacent check-in records in trajectory sequences through the attention mechanism.
GETNext 45 introduces the global mobility patterns of all users into the Transformer architecture to improve model prediction effects.
Graph-Flashback (27 combines Spatial-Temporal Knowledge Graph with the sequential model to enrich the representation each POI.
SNPM [46] learns the general characteristics of POIs by constructing a POI similarity graph and aggregating similar POIs.
Metrics. To evaluate the model performance, we utilize two of the most common metrics for the next POI prediction: Accuracy @k (Acc@k) and Mean Reciprocal Rank (MRR). Acc@k effectively measures whether the true label is present within the top-k predicted results. Here, we consider 5, and 10 to comprehensively assess the model's performance. MRR directly quantifies the average rank of the correct label among all predictions when the correct label is not within the top-k predictions, with higher values indicating better average prediction performance by the model.
Settings. We implement LoTNext using PyTorch 1.13.1 on a Linux server equipped with 384GB RAM, 10-core Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz, and Nvidia RTX 3090 GPUs. The embedding dimensions for POIs and users are set to 10, and the time embedding dimension is set to 6. For the Transformer architecture, we incorporate two multi-head attention mechanisms and encoder blocks. For the spatial decay rate B, we follow the settings of Flashback 43
Overall Performance. Table 2 shows the predictive performance of all baseline methods and LoTNext on two datasets. Based on Table we can draw the following conclusions:
On both public datasets, LoTNext outperforms all other state-of-the-art baseline methods across all metrics. Compared to the most recent and best-performing baseline method, SNPM, LoTNext achieves more significant improvements in Acc@1 1. These results indicate that LoTNext is better predicting long-tailed POIs that are less popular but highly relevant to specific users.
Utilizing graphs to model all user mobility patterns, thereby improving POI embeddings, repre- senting SNPM, GtraphFFlashbace and GETNext, significantly outperform sequential methods represented by LSTPM and DeepMove, which rely solely on an individual user's short-term and long-term interests to predict the user's location. However, the User-POI Interaction Graph has a large number of long-tailed nodes with a degree of or very small. LoTNext, through its long-tailed graph adjustment module, effectively filters these long-tailed nodes, thereby enhancing the model's 's predictive performance.
Performance on Long-Tailed Samples. To evaluate whether our model achieves accuracy improve- ment long-tailed samples, define samples with frequency less than 100 on Gowalla dataset
8

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as Long-tailed samples to test the model's s specific predictive performance on both long taig-taleddd and head samples. We compare LoTNext with Seaph-Flahback which provides the pre-trained model for ease of comparison. As shown in Figure 3(a) and Figure 3(b) LoTNext consistently outperforms Faaph- on both Acc@1 and MRR metrics, whether for head or long-tailed samples. Fur- thermore, Figure revelas reveals notable distinction in the prediction of long-tailed POIs, Loxt exhibits a roughly 6% higher propensity to predict long-tailed POIs compared to Flashbalabbc. This increment not only underscores the enhanced capacity of LoTNext to identify and anticipate long-tailed POIs but also demonstrates the efficacy of our methodology.
Sruch Faccback
Lothee:
Lollint
B320
Hod POI
Loi POI
91.0%
60.21%
2.00
Long ki
1046
Danpb-Fadbicck
LoTNeat:
(a) Acc@
(b) MRR.
(c) Proportion of the predicted POIs.
Figure 3: The performance comparison of the long-tailed and head POIs between LoTNext and Graph-Flase Cruph-Flashback on Gowalla dataset.
Ablation Study. To analyze the impact of different modules on LoTNext, conducted the following ablation settings: (1) without the Long-Tailed Tailed Graph Adjustment module (w/o LTGA), where we conducted with the raw graph without graph adjustment. (2) without the Long-Taile Tailed Loss Adjustment module (w/o LTLA), meaning we only used the original cross-entropy loss for testing. (3) without the original cross-entropp loss (w/o LoF), meaning we only
Long-Tailess Loss Adjustment module (LI.aa loss). (4) without the auxiliary prediction task module, we utilized the LTLA module and cross-entropy loss function, removing the auxiliary time prediction task, denoted w/o L
Table The performance comparison among the LoTNext and variants without some components.
Gowalla
Foursquare
Model
Acc@
Acc@5
Acc@
MRR
Acc@1
Acc@5
Acc 10
MRR
w/o LTGA
0.1617
0.3568
0.4419
0.2550
0.3020
0.6002
0.6783
0.4368
w/o LTLA
0.1544
0.3439
0.4266
0.2450
0.3014
0.5985
0.6758
0,4362
w/o Lce
0.1550
0.3455
0,4287
0,2462
0.3029
0.5989
0.6771
0,4365
w/o Lau
0.1609
0.3567
0.4344
0.2523
0.3039
0.5993
0.6769
0.4370
LoTNext
0.1668
0.3605
0.4429
0.2591
0.3155
0.6059
0.6812
0.4469
From 3 we have the following findings: (1) The embeddings obtained after the LTGA module contribute to the model's predictive performance. This is mainly because long-tailed POIs can be considered noise to some extent, and appropriately eliminating some noise helps with model prediction. (2) Utilizing only the original cross-entropy loss results in performance below SNPM, indicating that the strategy of considering the long-tailed distribution through the LTLA module is effective for improving model accuracy in identifying the most relevant items.
(3) The results using only the LITA loss show slightly higher metrics than results w/o LTLA, which suggests that the model may over-focus on long-tail data, leading to a decline in the recommendation performance for head data. For this reason, we consider incorporating the Lee to balance the recommendation performance between loig-tail data and head data. (4) Without the time prediction task, we observe a decline in the MRR metric, suggesting that temporal features play crucial role in helping the model capture the dynamic changes in user behavior.
Case Study: Learned POI Embedding. Figure 4 presents t-SNE visualizations of the embeddings for the four least frequently occurring POIs on Gowalla dataset. In Figure represennng LoTNext the embeddings of these fww-freuenccy POIs are more distinct and well-separated, indicating that LoTNext effectively captures the unique characteristies of these tail POIs. This clear separation demonstrates that LoTNext can learn meaningful representations even for the least frequent POIs, which is crucial for accurate prediction and recommendation. In contrast, F(a)re/4(a) <hhoiiiiiing showing Graph- Flashback's 's performance, reveals more overlapping and less distinct clusters for these low-frequency

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(a) Graph-Flashback
(b) LexNext.
Figure 4: The visualization tail POIs on Gowalla dataset. The color represents the POI frequency.
struggles to differentiate between the tail POIs, potentially leading less accurate predictions for these rarely visited locations.
POIs. This overlap suggests that
Prediction Long-Tailed Sample. Figure 5 provides visual comparison of sam- ple predictions made by the Graph-Flashback and LoTNext models on trajectory from the Gowalla dataset for user 5. Each POI the user's trajectory is identified by unique ID and visitation frequency, where number in parentheses represents the frequency of vis- its. In this specific trajectory. user visits sequence of POIs. For the given POI 934, LoT- Next accurately predicts the next POI to be 933, long-tai taigttal POI with visitation frequency of 94. In contrast, the Graph-Flashhaee model incor- rectly predicts the next POI to be 61, head POI with an extremely high visitation frequency of 2023. This is the same sample as the problem shown in Figure [] demonstrating the efficacy of LoTNext in capturing the user's actual movement pattern, which encompasses both frequently and infrequently visited POIs.
Case Study:
Easbbckk
2OST ITTC
Lamen
The oriv
G06V1000
LotNest
505 1942
Figure 5: Sample prediction from Gowalla dataset with Graph-Flashhack and LoTNext.
Conclusion
In this work, we propose LoTNext, a novel framework for human next POI prediction under long- tailed data distribution. Specifically, employ Long-Tailes Graph Adjustment module to mitigate the impact of long-tailed nodes within the User-POI Interaction Graph. Additionally, to balance the influence of long-tailed data in the loss, propose the Long- -ailed Loss Adjustment module to adjust the model's predicted logits and adaptively increase the weight of long-tailed samples. Moreover, we leverage the auxiliary prediction task to achieve spatial and temporal prediction synergy. Through comparisons with 10 state-of-tt of-the-art methods, we demonstrate the superiority of LoTNext over the most advanced approaches. A limitation of our approach lies in that LoTNeet's 's reliance extensive user trajectory data poses a potential risk for privacy breaches if deployed by certain institutions or companies, which could lead to negative social impacts. We plan to address it in future work.
Acknowledgments and Disclosure of Funding
This work was supported by JST SPRING Grant Number JPMJSP2108, JSPS KAKENHI Grant Num- ber JP24K02996. JST CREST Grant Number RMJCR21M2 including AIP challenge program, and Initiative on Recommendation Program for Young Researchers and Woman Researchers, Information Technology Center, The University of Tokyo.
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A Appendix supplemental material
A.1
Notations
The notations used in our paper are summarized as follows.
Table 4: Notation Table.
Symbol
Meaning
U.u P.p
User set and user
POI set and POI
T.t
Time slot and time slot
E
B..
ETT, Eps
Embedding of user, POI, timestamp, and position
x,
Embedding sequence w/o./with positional embedding
2.Z
Output the multi-head attention layer and transformer Refined output by spatial contextual attention layer Input the final fully connected layer Logit scores calculated by fully connected layer Trainable weight matrix
7,5'
0,0
W.
N
The number of sample
B
Batchsize
gin
User-POI Interaction Graph
g
Global Transition Graph
Vín Atn
Input node feature matrix and adjacent matrix of & Input node feature matrix and adjacent matrix G Attention scores
"Tr,""
Ag
9 A
Refined User-PO) Interaction Graph and adjacent matrix Node embedding of gdd and G
Bin ET
D D
Degree matrix of gd and GDV Denoised POI embedding
E.
dp, de, du
Hidden dimension of the POI, time and user Sequence length
b
Bias
Ground-truth indicator
1.
Logits and adjusted logits for each POI class
Time slot and the forecasted time slot
Spatial weight
Distance decay weight
(1,
Haversine distance between and
Small constant
Sigmoid function
Denoising threshold
Logit adjustment weight
Adjustment factor
E
Adjusted vector magnitude and its geometric mean
Adaptive weights for each sample
Learable loss weights
A.2 Computational Cost
In this section, we explore the computational cost of LoTNext. We selected three sequence-based and three graph-basee baselines to demonstrate the computational efficiency of our approach. Tisle the inference time for each deep learning model during the testing phase (running one training/testing instance, i.e., test time divided by batch size). We ensured that all models were executed on the same RTX 3090 GPU. Surprisingly, due to batch training, the graph-based methods generally run significantly faster than sequence-based methods. Deep is the fastest among the sequence- based methods, as it only considers calculating attention using historical trajectories. Compared to DeepMove, L.STPM further introduces a geographical relationship adjacency matrix to enrich the spatial context, making it slightly slower than DeepMove. STAN employs dual-layer attention
14

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architecture, with one attention layer aggregating spatiotemporal correlations within user trajectories and the other selecting the most likely next POI based on weighted check-ins, resulting the longest inference time for STAN.
Table 5: Comparison of computational cost. Each method is benchmarked on the same NVIDIA GeForce RTX 3090 GPU.
Method
Inference Time (10 Seconds)
DeepMove (Sequence-based)
1,422
LSTPM (Sequence-based)
3,417
STAN (Sequence-bare based)
2887.809
GETNext (Graph- basedb
3.824
Graph- Hraph-Flashbakk Straph-based
0.0918
SNPM (Graph-based) based)
0.491
LoTNext (Graph-based)
0.257
In the graph-based methods, GETNext introduces additional computational overhead due to the need for extra POI candidate probability reorganization based on transition attention during the final prediction stage. SNPM requires extra computation time due to the search for similar neighborhoods within the graph. As for our LoTNext, it requires more time because LoTNext includes graph denoising and an auxiliary temporal prediction task. However, Table and Table 3 demonstrate the effectiveness of our proposed modules, even at the cost of some computational time. Thus, considering that LoTNext encompasses more processing steps and overall accuracy, the increase in inference time is still acceptable.
compared to Graph-Flashback
A.3
Hyperparameter Analysis
0.26
0.66
0.44
Berfomance
0.24
0.24
0.42
0.47
0.22
Acc@1 MAR
Acc@1
0.40
Acc@l MAR
0.40
Acc@1
0.20
020
MAR
0.38
0.36
0.38
MRR
0.18
0.18
0.34
0.34
0.16
0.32
0.32
0.1
0.3
0.5
0.7
0.9
1.0
1.2
1.6
2.0
0.1
0.3 0.5 0.7 0.9
1.0 12 144.6 18 2.0
Paramater 6
Parameter T
Paramater
Farameter T
(a) The impact of parame-
(b) The impact of parame-(c) The impact of (d) The impact of pa- Gowalla dataset. rameter d on Foursquare rameter Foursquare dataset.
ter 8 on Gowalla dataset.
dataset.
Figure 6: Impact of denoising thresholds ō and logit adjustment weight
We conduct hyperparameter sensitivity experiments on the Long-Tailed Graph Adjustment module's threshold 8 and the weight 7 of the logit adjustment module to identify the optimal parameter values on Gowalla and Foursquare datasets. We first experiment with a range of thresholds á from 1.1 to 0.9 in increments of 0.2, which controls the sensitivity of the model the long-tailes distribution by filtering less significant edges in the graph. The results, shown in Figure 6(a) for Gowalla and Figure (6C) for Foursquare, indicate that Acc@I and MRR remain stable across different values, with the optimal threshold identified as 5 = 0.5. Next, we vary the logit adjustment weight from 1 to in increments of 0.2 test the model's performance in balancing class imbalances. Figure 6(b) and Figure reveal that 7 = 1.2 yields the best results on both datasets, suggesting moderate adjustment weight helps generalize better without overly amplifying rare classes. These consistent findings across both datasets underscore the robustness of 8 = 0.5 and = 1.2, highlighting the importance of hyperparameter tuning improving model accuracy and ranking metries for better prediction of user behavior in diverse datasets.
15

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A.4 Model Training Pseudo-code
Algorithm shows the pseudo-code of the LoTNext training process. our experiments, all training instances are processed through mini-batches.
Algorithm Pseudo-code training LoTNext
1: Input: User set U, POI set P. user check-in sequences Qu for each user a E
2: Output: Trained model parameters
3: Y Initialize randomly
while converge do
5: Construct graph G and G apply denoising, and leam node embeddings E and E by Eq. Graph Adjustment
Compute denoised embedding EP based on E and E Calculate time and user embeddings E" _ and construct embedding sequence X Embedding Initialization
Embedding Denoising
7:
8:
Transform imput X to X
Positional Encoding
9:
Calculate Transformer encoder output by Eq. for each POI ps: in sequence do
b Transformer Encoder
10:
11:
Calculate spatial weight wi by Eq.
D. Spatial Weight Calculation Output Refinement
12:
Refine output 2n by Eq.
13:
end for
14:
Calculate fuseed output O and logits T.
Prediction Layer
15:
Compute cross-entropy loss LCE by Eq.
Loss Calculation
16:
Adjust logits using a; and recompute logits & by Eq.
Logit Adjustment
17:
Calculate adaptive weights o" and overall loss LETA by Eq. 10
14
b Loss Adjustment
18:
Compute auxiliary loss LAuz by Eq. (15)
Auxiliary Loss
19:
Update parameters y by minimizing joint loss IoJoi by Rq. 16
Parameter Update
20:
end while
21:
return
16

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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction effectively summarize the paper's 's main contri- butions and the scope of the study, which focus improving POI prediction through the proposed LoTN method. This method addresses challenges related to the long-tailed distribution of location visitations. The main claims include the development of the Long- Tailed Loss Adjustment module, along with
Tailed Graph Adjustment module and the Long-Tailed the incorporation of auxiliary prediction tasks enhance model generalization and accuracy. These elements are precisely reflected in the detailed methodology and results sections of the paper, where the effectiveness of LoTNext demonstrated real-world datasets, significantly outperforming existing methods. Thus, the abstract and introduction provide a clear and accurate preview of the paper's technical content and its contributions the field.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
The paper explicitly discusses the limitations of the proposed method, Lott It acknowledges that the model does not account for emergent events such as vehicle accidents and extreme weather conditions, which are typical examples of long-tail data that can affect human mobility and, consequently, the model's robustness. Additionally, the paper highlights the privacy concerns associated with the extensive use of user trajectory data, acknowledging the potential risks the model is deployed by certain entities. This discussion in the paper ensures that readers are fully aware of the conditions under which the model was tested and its potential limitations in real-world scenarios.
Justification:
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
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The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: The paper does not include theoretical results or formal proofs as it primarily focuses on the application of deep learning techniques to the problem of the next POI prediction. The mathematical formulations presented within the paper represent specific operational processes within the deep learning model rather than propositions requiring formal proofs. These formulations are used to describe the architecture and function of the proposed Lext method, including its components like the Long-Tailed module and the Long- Tailed Loss Adjustment module. Since the paper's 's contributions are empirical and methodological rather than theoretical, it does not involve the derivation of theorems or the necessity of providing rigorous proofs.
Tailed Graph Adjustment
Guidelines:
The answer NA means that the paper does include theoretical results. All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: The paper provides the detailed information required for the reproducibility of the main experimental results. describes the datasets used, namely the Gowalla and Foursquare datasets, including the criteria for data filtering and splitting for training and testing purposes. It also specifies the baselines against which the proposed LoTNext method is compared, encompassing a comprehensive list of state-of-the-art methods. Furthermore, the paper outlines the evaluation metrics used, such as Accuracy k and Mean Reciprocal Rank (MRR), and explains their significance in the context of the next POI prediction. Additionally, detailed descriptions of the experimental settings. including hardware specifi- cations and software versions, are provided. This level of detail the experimental setup, methodology, and evaluation ensures that other researchers can replicate the study and verify the claims made about the performance of the LoTNext method. The disclosure of this information supports the reproducibility of the research, adhering to the standards required for scientific verification and validation.
Guidelines:
The answer NA means that the paper does not include experiments.
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If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. w
For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
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(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm. (b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g.. with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. the case of closed-source models, it may be that access to the model is limited in some way (e.g.. registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
The paper grants open access both the code and data necessary for repro- ducing the main experimental results, as stated in the abstract and introduction sections. The code for the proposed LoTNext method is available at a specified URL, hosted on public repository which maintains anonymity as per submission guidelines. This repository includes detailed instructions on setting up the environment, executing the code, and repro- ducing the experiments conducted with the LoTNext method. Additionally, the datasets used, Gowalla and Foursquare, are publicly available and well-documented, allowing re- searchers to access and use them for replication purposes. Instructions for data preprocessing and setup are clearly provided, ensuring that other researchers can faithfully replicate the study's findings. This comprehensive provision of resources supports the transparency and reproducibility of the research, aligning with the conference's 's guidelines for open access to data and code.
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Please see the NeurIPS code and data submission guidelines (https://nips.co co/
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The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ wtt.ps.cc/publc////ge//
son.oodeeuuiissiooooooooooo www.:iiiiisiioooooioooocoo for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if' applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental Setting/Details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
The paper provides comprehensive details about the experimental setup neces- sary understand and reproduce the results. It specifies the data splits used for training and testing, with 80% of the data used for training and the remaining 20% for testing. The paper also describes the hyperparameters employed, including the dimensions for user and POI embeddings, and the configuration of the Transformer architecture used in the model.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment Statistical Significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
To ensure the reproducibility of our experimental results, we fixed the ran- dom seed during the data splitting and model execution phases. As result, we did not report statistical significance measures such as error bars, confidence intervals, or statistical significance tests.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
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For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative enror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: The paper provides detailed information in Section about the compute resources used for the experiments, including the type of compute workers (Linux server with Intel Xeon Silver 4210R CPU and Nvidia RTX 3090 GPUs), memory (384GB RAM), and the software environment (PyTorch 1. 1.13.1). The specifications for the embedding dimensions, the Transformer architecture, and the spatial decay rate are also clearly described, ensuring that the experiments can be accurately reproduced.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make into the paper).
9. Code Of Ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer:
[Yes]
Justification: We read the NeurlPS Code of Ethics carefully and ensure we follow all the requirements.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. if there is special consid- cration due to laws or regulations their jurisdiction..
Broader Impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The paper addresses both the potential positive and negative societal impacts of the proposed LoTNext method. On the positive side, the method aims to enhance personalized navigation, optimize recommendation systems, and facilitate urban mobility and planning. These improvements could significantly enhance the quality of life for city residents by making navigation and planning more efficient and tailored to individual needs. On the negative side, the paper acknowledges specific risks associated with the deployment of LoTNext, particularly concerning privacy. The reliance on extensive user trajectory data could potentially lead to privacy breaches if the model employed by institutions companies without stringent data protection measures. This could have adverse social impacts, such as unauthorized surveillance or misuse of personal data. The paper's 's discussion of these potential harms demonstrates awareness of the broader implications of deploying
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such technology in real-world settings. By addressing both the potential benefits and risks, the puper provides a balanced view of its societal impact, complying with ethical standards for transparency and responsibility in Al research. This acknowledgment of both positive and negative impacts ensures that readers and potential users are well-informed about the capabilities and limitations of the proposed method.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (c.g.. deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: Our paper poses no such risks. We did not use any pre-trained models, and all datasets are sourced from publicly available data, which are commonly used in related work.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have a high risk for misuse or dual- use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: We have properly credited the creators and original owners of all assets used in the paper, including code, data, and models. The sources and relevant citations are clearly indicated in the paper. Additionally, the licenses and terms of use for these assets have
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been explicitly mentioned and properly respected. We have used publicly available datasets, citing the original papers that produced them, and included the specific versions and URLs where applicable. The licenses, such as CC-BY 4.0, have been noted, ensuring compliance with the terms of use.
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset used and, possible, include a URL
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If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New Assets
Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Question:
Answer: [Yes]
The new assets introduced in the paper are thoroughly documented. De- tailed documentation is provided alongside the assets, including information about the dataset/codeemmddel training procedures, licenses, and limitations. This ensures that other researchers can effectively utilize and build upon our work.
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Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
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At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and Research with Human Subjects
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Question:
Answer: [NA]
Justification: Although our research involves predicting human next points of interest (POI), our data sourced from publicly available datasets, which are commonly used in related work. The sources and relevant citations are clearly indicated in the paper, making this question not applicable to our study.
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The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
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According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
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Reviews:
[{"review_id": "UurjdhAYLf", "reviewer": "Reviewer_7ppu", "summary": "This study proposes the Long-Tail Adjusted Next Point-of-Interest Prediction (LoTNext) framework. By combining a Long-Tailed Graph Adjustment module and a Long-Tailed Loss Adjustment module, it reduces the impact of long-tailed nodes in the user-POI interaction graph and adjusts loss through logit score and sample weight adjustment strategies. Experimental results show that LoTNext outperforms several existing methods on two real-world datasets.", "questions": "1. How do the authors balance the performance between head POIs and long-tail POIs? In other words, how do they enhance the performance of long-tail POIs without compromising the performance of head POIs? As shown in Figure 3(c), the proportion of predicted long-tail POIs is relatively high. Does this affect the prediction of head POIs?\n2. In the experimental section, it would be beneficial to show the performance of the proposed method and the baselines on both head POIs and long tail POIs to enhance the persuasiveness of the conclusions.", "limitations": "The authors have already pointed out a limitation of this method: LoTNext relies on extensive user trajectory data, which, if deployed by certain institutions or companies, may pose a potential risk of privacy breaches, potentially leading to negative social impacts.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The structure and organization of this paper are well-designed, and the writing is clear and easy to comprehend.\n2. This paper investigates the long-tail problem by proposing a general framework for next POI recommendation, filling the gap in addressing the long-tail issue in POI recommendation. This work is meaningful and valuable.\n3. To enhance the readability of the paper, the authors provide detailed results analysis, parameter settings, and the motivation behind the design of each module in the appendix.", "weaknesses": "1.  In the related work section, the authors review common methods for addressing the long-tail problem in recommendation systems. Since this paper focuses on addressing the long-tail problem, adding several baselines that tackle the long-tail issue in recommendation systems (e,g,, [1]) would better demonstrate the effectiveness of the proposed method.\n2. The novelty of this paper is not very strong. The long tail effect of check-in data, such as the POI frequency distributions, has been studied before. \n3. Additional comparative analyses should be included to illustrate the shortcomings of baselines in handling the long-tail issue. For instance, comparing the proposed model's performance with all baselines (not just Graph-Flashback) on long-tail POIs would better demonstrate its effectiveness in addressing the long-tail problem.\n4. The experimental results are not convincing enough, as the compared methods are not the SOTA method. More recent baselines should be compared (e.g., [2-4]).\n\n[1] Meta graph learning for long-tail recommendation, SIGKDD, 2023.\n[2] EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation， SIGIR-23\n[3] Adaptive Graph Representation Learning for Next POI Recommendation， SIGIR-23\n[4] Spatio-Temporal Hypergraph Learning for Next POI Recommendation， SIGIR-23", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer 7ppu:\n**Weaknesses:**\n> Q1. In the related work section, the authors review common methods for addressing the long-tail problem in recommendation systems. Since this paper focuses on addressing the long-tail problem, adding several baselines that tackle the long-tail issue in recommendation systems (e,g, [1]) would better demonstrate the effectiveness of the proposed method.\n\n**A1:** Thank you for your suggestions and valuable references. Compared with MGL [1], our task has several difference:\n1. MGL [1] primarily addresses the click problem under long-tail problem, i.e., whether or not to interact with an item, without considering the temporal aspect. In contrast, our task in human mobility prediction inherently involves spatial-temporal features, such as check-in times and POI geographic locations.\n2. The input of MGL is the user's feedback on item ratings, clicks, etc., without the temporal features. However, our model input is a trajectory sequence with spatial-temporal features, hence MGL is difficult to deploy under our datasets.\n\nDue to time and computation resource constraints, we add the AGRAN [3] model as our new baseline. Additionally, we also include the TPG [5] as a new baseline suggested by reviewer Et5T. The specific results are shown in **\"global\" response** (please see above).\n\n> Q2. The novelty of this paper is not very strong. The long tail effect of check-in data, such as the POI frequency distributions, has been studied before.\n\n**A2:** Thanks for your insightful comments. Different with previous work, our paper has several novel points: \n1. We are the **first work** to propose a solution specifically for the **long-tail problem in the human mobility prediction field**. \n2. Our approach includes a graph adjustment module, a loss adjustment module, and a time prediction auxiliary module. Through these novel modules, we significantly enhance the model's performance in predicting long-tail data (from Table 1 and Figure 3) . \n3. Previous studies have only treated check-in frequency as a data feature of POIs, which is fundamentally different from our approach.\n\n> Q3. Additional comparative analyses should be included to illustrate the shortcomings of baselines in handling the long-tail issue. For instance, comparing the proposed model's performance with all baselines (not just Graph-Flashback) on long-tail POIs would better demonstrate its effectiveness in addressing the long-tail problem.\n\n**A3:** Thanks for your detailed comments. Due to time constraints, we apologize for not being able to compare the performance of all baselines on tail data. Given our inclusion of the graph adjustment module, we focused on graph-based baselines. As shown in Table 1, Graph-Flashback and SNPM are the best-performing graph-based baselines. Following your suggestion, we included SNPM for comparison. The detailed results are shown in Figure I attached in the **\"global\" response** PDF file (please see above). \n* From Figure I, we can find on both the Gowalla and Foursquare datasets, LoTNext outperforms other baselines in predicting tail data, particularly evident in the MRR metric. \n* Moreover, we do not compromise the prediction of head data, as LoTNext also shows superior performance compared to the other two baselines in head data prediction.\n\n> Q4. The experimental results are not convincing enough, as the compared methods are not the SOTA method. More recent baselines should be compared (e.g., [2-4]).\n\n**A4:** Thank you for your suggestions. Please refer to the answer of Q1 in detail.\n\n**Questions:**\n> Q5. How do the authors balance the performance between head POIs and long-tail POIs? In other words, how do they enhance the performance of long-tail POIs without compromising the performance of head POIs? As shown in Figure 3(c), the proportion of predicted long-tail POIs is relatively high. Does this affect the prediction of head POIs?\n\n**A5:** Thank you for your valuable questions. In our loss adjustment module, the logit score adjustment module prevents the model from overly focusing on head data, while the sample weight adjustment module balances head and tail data. \n* First, the logit score adjustment module enhances the weights of tail samples by POI visit frequency.\n* Next, the sample weight adjustment module calculates the vector magnitude of each sample, and comparing it to a baseline magnitude, we penalize samples that are less beneficial for the model's predictions. \n* In summary, regardless of whether the data is head or tail, if the model determines that a sample contributes significantly overall, we increase its weight in the loss to ensure the model treats head and tail samples equitably. \n* In addition, as you mentioned, according to Figure 3(c), LoTNext predicts a higher proportion of tail samples. However, this does not affect the model's overall performance. Figures 3(a) and (b) and Figures Ⅰ(a), (b), (c), and (d) in the **\"global\" response** PDF file (see above) demonstrate that our LoTNext outperforms other baselines in predicting both head and tail data.\n\n> Q6. In the experimental section, it would be beneficial to show the performance of the proposed method and the baselines on both head POIs and long tail POIs to enhance the persuasiveness of the conclusions.\n\n**A6:** Thank you for your suggestions. Figure 3(a) and (b) include the relevant experiments. Additionally, in response to your comments on Q3 and Q5, we have incorporated other baselines for comparison. Please refer to our responses to Q3 and Q5 for detailed information.\n\n*If you are satisfied with our responses, we would be grateful if you could kindly check the positive remarks of other reviewers and consider raising the score.*", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LoALdN9Wdj", "reviewer": "Reviewer_Et5T", "summary": "This paper introduces the LoTNext framework, which is designed to improve the prediction of human mobility patterns, specifically addressing the challenge of long-tail distribution in POI visitations. The authors propose a novel approach that includes a Long-Tailed Graph Adjustment module and a Long-Tailed Loss Adjustment module, along with an auxiliary prediction task, to enhance the model's ability to predict less frequently visited POIs. The paper demonstrates the effectiveness of LoTNext through comprehensive experiments on two real-world datasets, showing significant improvements over existing state-of-the-art methods.", "questions": "(1) Why does cutting off the long tail in the user-POI graph for noise reduction, followed by using loss to reintroduce the long tail, theoretically improve long tail prediction?\n\n(2) In Figure 4, how should we interpret \"four least frequently occurring pois\"? Does it refer to only the four POIs with the lowest frequencies? I guess many POIs only appear once.", "limitations": "N/A", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I like the research gap proposed by this paper. This is a worthwhile issue to study.", "weaknesses": "(1) The evaluation could be expanded to include a broader range of metrics to further validate the generalizability of the LoTNext framework.\n(2) It's better to have more explainability related experiments.\n(3) A more detailed literature review is needed (at least in the appendix) so that the novelty of the method could be better evaluated. \n(4) The comparison methods used are somewhat outdated. Why didn't you use the latest methods, such as TPG (https://arxiv.org/abs/2304.04151) or LLM-Move (https://arxiv.org/pdf/2404.01855), for comparison?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer Et5T:\n**Weaknesses:**\n> Q1. The evaluation could be expanded to include a broader range of metrics to further validate the generalizability of the LoTNext framework.\n\n**A1:** Thank you for valuable suggestions. We add Normalized Discounted Cumulative Gain (NDCG) as a new metric, the specific results are shown in **\"global\" response** (please see above). \n\n> Q2. It's better to have more explainability related experiments.\n\n**A2:** Thanks for your comments. To make our model prediction results more interpretable, we visualized the prediction results of LoTNext and Graph-Flashback on the Gowalla dataset, as shown in Figure II attached in the **\"global\" response** PDF file (please see above). In Figure II, the blue circles represent the user's historical trajectories, the green circles indicate the model's predicted top-20 POIs, and the red circle marks the actual next POI. \n* From Figure II, we can observe the predicted POI are **more dispersed and cover a wider area** in the LoTNext compared to the Graph-Flashback. This indicates that LoTNext provides a broader prediction range within the user's historical trajectory, potentially capturing a wider variety of POIs. \n* The LoTNext model's predictions **include the actual next POI** within its top-20 suggestions, demonstrating its higher accuracy. In contrast, the Graph-Flashback model's predictions are more concentrated in fewer locations, which suggests it might focus on more predictable, routine movements but could **miss out on less frequently visited POIs**. \n* In summary, LoTNext not only provides a **better balance** between exploring different areas and accurately predicting the next POI but also successfully predicts the actual next POI, compared with Graph-Flashback.\n\n> Q3. A more detailed literature review is needed (at least in the appendix) so that the novelty of the method could be better evaluated.\n\n**A3:** Thanks for your helpful advice. We will include a more detailed literature review in the final version, covering topics such as LLM-based human mobility prediction and other long-tailed learning methods. In addition, we would like to conclude our novelty as below:\n* Due to the **input** of the human mobility prediction task being **not individual samples** but **sequences**, many long-tailed learning methods from CV and recommendation field are **hard to deploy** in the human mobility prediction task.\n* In the human mobility prediction community, the current focus is on improving model prediction accuracy from the perspective of graph optimization learning, completely **overlooking the long-tail feature of human check-in datasets**.\n* **Our study is the first to propose a general framework for human mobility prediction under the long-tail problem**. \n* **LLM-based human mobility prediction** is still in its early stages and holds significant potential. We believe that exploring and addressing the **long-tail problem based on the LLM framework** is a very interesting direction for future research.\n\n> Q4. The comparison methods used are somewhat outdated. Why didn't you use the latest methods, such as TPG or LLM-Move, for comparison?\n\n**A4:** Thank you for your detailed comments and valuable paper recommendations. **LLM-Move [6]** is based on large language models (LLMs) for human mobility prediction tasks. However, **LLM-Move [6]** requires category as additional semantic information, which is not applicable in our dataset.\n\nTherefore, we add **AGRAN [3]** suggested by reviewer 7ppu and **TPG [5]** as our new baselines, please refer to the **\"global\" response** (please see above) for a detailed experiment results explanation.\n\n**Questions:**\n> Q5. Why does cutting off the long tail in the user-POI graph for noise reduction, followed by using loss to reintroduce the long tail, theoretically improve long tail prediction?\n\n**A5:** Thank you for your questions. We do not reintroduce the long tail through the loss function; instead, we ensure that the model does not overly focus on head data. \n* First, the long-tailed loss adjustment module increases the attention to tail data via logit score adjustment. \n* Next, the sample weight adjustment module calculates the vector magnitude of all samples and compares them to the geometric mean magnitude to identify those samples that contribute significantly to the model. \n* This approach balances the learning of features from both head and tail data.\n\n> Q6. In Figure 4, how should we interpret \"four least frequently occurring pois\"? Does it refer to only the four POIs with the lowest frequencies? I guess many POIs only appear once.\n\n**A6:** Thank you for pointing out this confusing problem. We apologize for the unclear explanation here. Specifically, we divide the least frequently occurring POIs into four groups using the intervals [1,10), [10,20), [20,30), and [30,40). Then, we perform t-SNE visualization on these groups.\n\n*If our responses could address your concerns, please kindly consider raising the score.*", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kvPadEboia", "reviewer": "Reviewer_mZab", "summary": "The paper presents the Long-Tail Adjusted Next POI Prediction (LoTNext) framework to address the long-tail problem in next POI prediction. This problem refers to the uneven spatial and temporal distribution of POI visits, making it challenging for prediction models to predict less frequently visited POIs. LoTNext combines a Long-Tailed Graph Adjustment module to reduce the noise and impact of long-tailed nodes in the user-POI interaction graph and a Long-Tailed Loss Adjustment module to balance the loss between head and tail POIs. Additionally, an auxiliary prediction task is employed to enhance generalization and accuracy. The proposed method was evaluated on two real-world trajectory datasets, Gowalla and Foursquare, where it significantly outperformed existing methods.", "questions": "How does the complexity of LoTNext affect its scalability and real-time performance in practical applications? Are there any simplifications or optimizations that can be applied without significantly compromising performance?", "limitations": "The authors have adequately addressed the limitation of their work, particularly the potential privacy risks associated with the extensive use of user trajectory data.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- LoTNext introduces a unique combination of graph adjustment and loss adjustment modules to tackle the long-tail problem, which is a significant contribution to the field of human mobility prediction.\n- The framework is evaluated on two real-world datasets and compared with ten existing methods, demonstrating superior performance across multiple metrics.\n- The paper provides a thorough explanation of the methodology, including the embedding generation, transformer encoder, spatial contextual attention layer, and the overall optimization process, making it reproducible and transparent.", "weaknesses": "- The proposed model is complex and involves multiple components and adjustments, but it is not clear how computationally expensive it would be to make predictions in services and elsewhere.\n- The model performed well on the dataset used, but it is unclear under what conditions the proposed method will perform well, such as visit intervals and frequency of visits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer mZab:\n**Weaknesses:**\n> Q1. The proposed model is complex and involves multiple components and adjustments, but it is not clear how computationally expensive it would be to make predictions in services and elsewhere.\n\n**A1:** Thank you for your insightful questions. Table 3 in the appendix lists the inference time for each deep learning model during the testing phase (running one training/testing instance, i.e., test time divided by batch size). For a more intuitive comparison of computational cost, we copied Table 3 as Table Ⅰ below. We selected three sequence-based and three graph-based baselines to demonstrate the computational efficiency of our approach. We ensured that all models were executed on the same RTX 3090 GPU. \n\n1. Sequence-based baseline\n\n* **DeepMove** is the fastest among the sequence-based methods, as it only considers calculating attention using historical trajectories. \n* Compared to **DeepMove**, **LSTPM** further introduces a geographical relationship adjacency matrix to enrich the spatial context, making it slightly slower than **DeepMove**. \n* **STAN** employs a dual-layer attention architecture, with one attention layer aggregating spatiotemporal correlations within user trajectories and the other selecting the most likely next POI based on weighted check-ins, resulting in the longest inference time for **STAN**.\n\n2. Graph-based baseline\n* Due to batch training, the graph-based methods generally run significantly faster than the sequence-based methods. \n* **GETNext** introduces additional computational overhead due to the need for extra POI candidate probability reorganization based on transition attention during the final prediction stage. \n* **SNPM** requires extra computation time due to the search for similar neighborhoods within the graph. \n* **LoTNext** requires more time to run compared to **Graph-Flashback** because **LoTNext** includes graph denoising and an auxiliary temporal prediction task. Table 1 and 2 (from the original paper) demonstrate the effectiveness of our proposed modules, even at the cost of some computational time. Therefore, considering that **LoTNext** encompasses more processing steps and overall accuracy, the increase in inference time is still acceptable.\n\nTable Ⅰ Comparison of computational cost. Each method is benchmarked on the same NVIDIA\nGeForce RTX 3090 GPU.\n|Method| Inference Time ($10^{-3}$ Seconds) |\n|-|-|\n|DeepMove (Sequence-based)|1.422|\n|LSTPM (Sequence-based)|3.417|\n|STAN (Sequence-based)|2887.809|\n|GETNext (Graph-based)|3.824|\n|Graph-Flashback (Graph-based)|0.0918|\n|SNPM (Graph-based)|0.491|\n|LoTNext (Graph-based)|0.257|\n\n\n> Q2. The model performed well on the dataset used, but it is unclear under what conditions the proposed method will perform well, such as visit intervals and frequency of visits.\n\n**A2:** Thanks for proposing this potential concern. We further analyzed the visit intervals for all users in the two datasets. We firstly categorized the intervals into eight hourly ranges: [0, 1], (1, 3], (3, 6], (6, 12], (12, 24], (24, 48], (48, 72], and (72, ∞). The detailed results are shown in Figure III attached in the **\"global\" response** PDF file (please see above).\n\n* Based on Figure III, approximately 30% of the check-in intervals in the Gowalla dataset are less than or equal to one hour, with half of the intervals being within 6 hours. \n* In contrast, the Foursquare dataset has a more evenly distributed interval range, with half of the intervals within 12 hours, and around 20% of the intervals exceeding 72 hours. \n* These **complex and uneven time distributions** highlight the importance of our time auxiliary prediction module.\n\nRegarding the frequency of visits, Figures 7(a) and 7(b) in the appendix show the relevant results. \n* We can observe that the maximum occurrence frequency of POIs in the Foursquare dataset is ten times that of the Gowalla dataset, and the number of extreme values is also higher in the Foursquare dataset.\n* Additionally, it is clear that the majority of POIs in both datasets have very low occurrence frequencies. \n* These results intuitively reflect **the long-tail nature of both datasets**.\n\n**Questions:**\n> Q3. How does the complexity of LoTNext affect its scalability and real-time performance in practical applications? Are there any simplifications or optimizations that can be applied without significantly compromising performance?\n\n**A3:** Thank you for the inspiring question. We acknowledge that using multiple deep learning methods can indeed affect the scalability and real-time performance of the model during actual deployment. However, our LoTNext model has adaptable simplification measures. \n* We can conduct **LightGCN** instead of traditional GCN can improve computation speed. The performance of **LightGCN** has been validated in many related works, ensuring it does not compromise the performance of GCN as a graph learning module. \n* We can conduct **graph pre-training** to enhance training efficiency. A **well-pretrained graph representation** might eliminate the need for our graph adjustment module without affecting the overall model performance, thereby speeding up the training process.\n* In addition, the loss adjustment module **do not involve overly complex calculations**, thus not significantly increasing computational overhead. \n\n*If the above discussions address your concerns, we would greatly appreciate it if you could consider increasing the score.*", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lklW8T0Fw4", "reviewer": "Reviewer_JhGF", "summary": "This paper addresses the challenge of predicting less frequently visited points-of-interest (POIs) in human mobility data, a problem known as the long-tail issue in spatial distribution. The authors introduce a new framework called Long-Tailed Adjusted POI Prediction (LoTNext), which includes two main components: long-tailed graph adjustment module and long-tailed loss adjustment module. Additionally, the framework employs an auxiliary prediction task to enhance the model's generalization and overall accuracy. The effectiveness of LoTNext is demonstrated through experiments on two real-world trajectory datasets, where it significantly outperforms existing state-of-the-art methods in human mobility prediction.", "questions": "1. I am curious if the structure of preliminary model structure can bring significant influence to the final results.\n2. According to my experiments, the detailed value setting of $\\lambda_1$,  $\\lambda_2$, and  $\\lambda_3$ in Eq. 16 can lead to the obvious variation of the ultimate model performance. However, the hyperparameter sensitivity experiments on this point is missing. More discussions and empirical results regarding this are welcomed.", "limitations": "See above weaknesses and questions.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The code has been provided, which makes the reproducibility of this paper good.\n2. The paper is generally well-writern and easy to follow.\n3. The proposed method is motivation-grounded.", "weaknesses": "1. The presentation quality of this paper can be further enhanced.\n2. The authors are encouraged to conduct experiments on more datasets and provide more detailed analysis.\n3.  This paper can supplement more theoretical analysis to guarantee the proposed method's effectiveness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to Reviewer 7hGF:\n**Weaknesses:**\n> Q1. The presentation quality of this paper can be further enhanced.\n\n**A1:** Thank you for your suggestions. We will check and polish the entire paper to ensure the presentation can be more clearly.\n\n> Q2. The authors are encouraged to conduct experiments on more datasets and provide more detailed analysis.\n\n**A2:** We appreciate your comments. We plan to introduce more datasets in our future work to more comprehensively evaluate our model. In addition, to better evaluate our model's performance, we add two latest baselines on the existing dataset. Please refer to the **\"global\" response** (please see above) for detailed results.\n\n> Q3. This paper can supplement more theoretical analysis to guarantee the proposed method's effectiveness.\n\n**A3:** Thank you for bringing this problem to our attention. We want to discuss **the motivation behind our sample weight adjustment module design** from a theoretical perspective.\n* For the metric Acc@1 we have: $\\text{Acc} = \\frac{1}{N} \\sum_{i=1}^{N} \\mathbf{1}(\\hat{y}^i = y^i)$, where $\\mathbf{1}(\\cdot)$ is the indicator function. On long-tail datasets, using the arithmetic mean would result in **very small penalties for tail data with low accuracy**. For example, the average of 0.01 and 0.99 is 0.5.\n* In contrast, the harmonic mean: $H = \\frac{n}{\\sum_{i=1}^{n} \\frac{1}{x_i}}$, which yields a value of 0.02 for 0.01 and 0.99, demonstrating **higher sensitivity to small values**, which is beneficial for penalizing tail data. However, the harmonic mean is defined by **reciprocals**, which increases the **difficulty of model optimization**. \n* Similarly, the geometric mean of 0.01 and 0.99 is 0.10, which is also very **sensitive to small values**. Its simple definition allows the model to** optimize effectively** based on it. Therefore, we finally choose geometric mean to calculate baseline magnitude.\n\nWe hope these discussions can address your concerns.\n\n**Questions:**\n> Q4. I am curious if the structure of preliminary model structure can bring significant influence to the final results.\n\n**A4:** Thanks for pointing out this potential problem. We try to remove the **Spatial Contextual Attention module (SC Att.)** and replace the **Transformer** with the most traditional **RNN** model to evaluate its impact on the overall model. The results are shown below.\n\nTable Ⅰ. The different preliminary model performance comparison on Gowalla dataset.\n|Method/Metric|Acc@1|Acc@5|Acc@10|MRR|NDCG@5|NDCG@10|\n|-|-|-|-|-|-|-|\n|With RNN|0.1529|0.3400|0.4210|0.2423|0.2511|0.2773|\n|Wtihout SC Att.|0.1596|0.3547|0.4376|0.2523|0.2619|0.2888|\n|**LoTNext**|**0.1668**|**0.3605**|**0.4429**|**0.2591**|**0.2686**|**0.2953**|\n\nTable Ⅱ. The different preliminary model performance comparison on Foursquare dataset.\n|Method/Metric|Acc@1|Acc@5|Acc@10|MRR|NDCG@5|NDCG@10|\n|-|-|-|-|-|-|-|\n|With RNN|0.2532|0.5242|0.6044|0.3778|0.3997|0.4258|\n|Wtihout SC Att.|0.3117|0.6040|0.6805|0.4410|0.4740|0.4994|\n|**LoTNext**|**0.3155**|**0.6059**|**0.6812**|**0.4469**|**0.4753**|**0.5001**|\n\n* We can observe that replacing the Transformer module with an RNN significantly impacts the performance, especially on the Foursquare dataset. This highlights the powerful modeling capabilities of the Transformer. \n* The RNN model under the optimization by our graph adjustment module, loss adjustment module, and other components has resulted in superior performance compared to other RNN-based models, such as STAN and Flashback. This demonstrates that our approach is both **generalizable and effective**. \n* Removing the Spatial Contextual Attention layer has a substantial effect on the Acc@1 results, which is the most critical metric for accurately assessing the model's predictive ability. \n* Therefore, **both modules are essential for the current model**.\n\n> Q5. According to my experiments, the detailed value setting of $\\lambda_1$, $\\lambda_2$, $\\lambda_3$ and in Eq. 16 can lead to the obvious variation of the ultimate model performance. However, the hyperparameter sensitivity experiments on this point is missing. More discussions and empirical results regarding this are welcomed.\n\n**A5:** Thanks for your inspiring questions. We **fixed the weights** of three different $\\lambda$ to test the model performance, and the results are as follows.\n\nTable Ⅲ. The model performance comparison of the parameter $\\lambda$ on Gowalla dataset.\n|Weight/Metric|Acc@1|Acc@5|Acc@10|MRR|NDCG@5|NDCG@10|\n|-|-|-|-|-|-|-|\n|[0.5, 0.25, 0.25]|0.1641|0.3595|0.4408|0.2580|0.2676|0.2950|\n|[0.25, 0.5, 0.25]|0.1668|0.3596|0.4424|0.2588|0.2680|0.2951|\n|[0.25, 0.25, 0.5]|0.1653|0.3594|0.4421|0.2584|0.2680|0.2952|\n|[0.33, 0.33, 0.33]|0.1580|0.3551|0.4386|0.2516|0.2614|0.2885|\n|**Dynamic**|**0.1668**|**0.3605**|**0.4429**|**0.2591**|**0.2686**|**0.2953**|\n\nTable Ⅳ. The model performance comparison of the parameter $\\lambda$ on Foursquare dataset.\n|Weight/Metric|Acc@1|Acc@5|Acc@10|MRR|NDCG@5|NDCG@10|\n|-|-|-|-|-|-|-|\n|[0.5, 0.25, 0.25]|0.3082|0.6037|0.6812|0.4419|0.4689|0.4942|\n|[0.25, 0.5, 0.25]|0.3153|0.6059|0.6811|0.4469|0.4734|0.4983|\n|[0.25, 0.25, 0.5]|0.3121|0.6055|0.6810|0.4452|0.4722|0.4974|\n|[0.33, 0.33, 0.33]|0.3131|0.6053|0.6812|0.4453|0.4720|0.4970|\n|**Dynamic**|**0.3155**|**0.6059**|**0.6812**|**0.4469**|**0.4753**|**0.5001**|\n\n* The value of $\\lambda$ does not significantly impact the performance. \n* When $\\lambda_2$ is maximum, indicating an increased weight for our LTA loss, the performance is optimal, second only to the dynamic parameters. \n* When the three $\\lambda$ values are equal, the model's performance on the Gowalla dataset shows a significant decline. This indicates that the model requires different emphasis on different losses to achieve further improvement.\n\n*If you believe our paper is qualified for NeurIPS, we would truly appreciate it if you could give us further support by increasing the score.*", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
# Response to All Reviewers:
We thank the reviewer for the very valuable, detailed and constructive feedback on our work. We especially thank the positive words:

* work is meaningful and valuable, worthwhile issue to study (Reviewer #7ppu & #Et5T)
* filling the gap in addressing the long-tail issue (Reviewer #7ppu & #Et5T)
* well-written, easy to comprehend and thorough explanation of the methodology (Reviewer #7ppu & #mZab & #JhGF)
* motivation explanation behind the design of each module (Reviewer #7ppu &#JhGF)
* detailed results analysis and superior performance across multiple metrics (Reviewer #7ppu & #mZab)
* significant contribution to the field of human mobility prediction (Reviewer #mZab)
* the reproducibility of this paper is good (Reviewer #mZab & #JhGF)

Due to word limitation, we conclude a similar question and answer it in response to your valuable comments. Thank you!

> Q1 Need to add more baselines, metrics and datasets. (Reviewer #7ppu & #Et5T)

**A1:** 
Based on the feedback from reviewers #7ppu and #Et5T, we have summarized six highly valuable and relevant papers, each with different focuses:

1. **MGL [1]** focuses on addressing the long-tail problem in the recommendation domain. The input of MGL is the **user's feedback** on item ratings, clicks, etc., without the temporal features. However, our model input is a **trajectory sequence** with spatial-temporal features, hence MGL is difficult to deploy under our datasets.
   
2. **EEDN [2]** proposes an enhanced network to tackle implicit feedback and cold-start issues in POI recommendation by leveraging latent interactions between users and POIs. However, its **input trajectories lack specific temporal factors**, making this work more akin to the sequential recommendation and slightly different from our task definition.
   
3. **AGRAN [3]** proposes an adaptive graph representation method, which explores the utilization of graph structure learning to replace static graphs.
   
4. **STHGCN [4]** proposes to capture the higher-order information including user trajectories and the collaborative relations among trajectories by hypergraph. However, the construction of hypergraph requires **categories semantic information**, which is not applicable in our dataset. 

5. **TPG [5]** proposes a framework that integrate temporal prompts and geography-aware strategies, overcoming encoding limitations of traditional methods.
   
6. **LLM-Move [6]** is based on large language models (LLMs) for human mobility prediction tasks. However, LLM-Move requires additional **category semantic information** as input, which is not applicable in our dataset.

In summary, we add AGRAN [3] and TPG [5] as new baselines to further validate the performance of our model. Additionally, we add Normalized Discounted Cumulative Gain (NDCG) as a new metric suggested by reviewer #Et5T (since the results of NDCG@1 and Acc@1 are same, we ignore it here). For better comparison, we only excerpt the best-performing baselines from the original paper. The detailed results are as follows:

Table Ⅰ. The performance comparison on Gowalla dataset.
|Method/Metric|Acc@1|Acc@5|Acc@10|MRR|NDCG@5|NDCG@10|
|-|-|-|-|-|-|-|
|Graph-Flashback|0.1495|0.3399|0.4242|0.2401|0.2497|0.2766|
|SNPM|0.1593|0.3514|0.4346|0.2505|0.2600|0.2872|
|AGRAN (new)|0.1005|0.2456|0.3154|0.1731|0.1764|0.1990|
|TPG (new)|0.1400|0.3071|0.3611|0.1948|0.2059|0.2374|
|**LoTNext**|**0.1668**|**0.3605**|**0.4429**|**0.2591**|**0.2686**|**0.2953**|

Table Ⅱ. The performance comparison on Foursquare dataset.
|Method/Metric|Acc@1|Acc@5|Acc@10|MRR| NDCG@5|NDCG@10|
|-|-|-|-|-|-|-|
|Graph-Flashback|0.2786|0.5733|0.6501|0.4109|0.4411|0.4661|
|SNPM|0.2899|0.5967|0.6763|0.4278|0.4480|0.4757|
|AGRAN (new)|0.1575|0.3736|0.4676|0.2600|0.2703|0.3008|
|TPG (new)|0.2321|0.4631|0.5493|0.3775|0.3891|0.4106|
|**LoTNext**|**0.3155**|**0.6059**|**0.6812**|**0.4469**|**0.4753**|**0.5001**|

* **AGRAN [3]** perform lower than the graph-based baselines such as **Graph-Flashback** and **SNPM**.  However, the deployed datasets from **AGRAN [3]** are based on Foursquare-Singapore and Gowalla-Nevada, which are **city-level** making adaptive graph learning more suitable. 
* Our datasets are **global-level**, as shown in the heatmap from Figure 6 in the appendix, which are significantly larger than city-level datasets. Therefore, it is challenging to adaptative build and learn embeddings from graphs on global-level datasets without relying on prior knowledge. These factors contribute to the lower performance of **AGRAN [3]** under our global human check-in datasets.

* **TPG [5]** performs better than **AGRAN [5]** and is very close to **STAN**. However, it requires knowing the exact time of the next location visit during prediction.  **LoTNext** does not need future moments as prompts and performs better.
* **TPG [5]** explicitly models the geography of longitude and latitude, which poses a potential risk of user privacy leakage. **LoTNext** calculates spatial contextual attention based on geographic distance differences, which has a lower risk of user privacy leakage.

**References**

[1] Wei, C., et al. Meta graph learning for long-tail recommendation. KDD 2023.

[2] Wang, X., et al. Eedn: Enhanced encoder-decoder network with local and global context learning for poi recommendation. SIGIR 2023.

[3] Wang, Z., et al. Adaptive Graph Representation Learning for Next POI Recommendation. SIGIR 2023.

[4] Yan, X., et al. Spatio-temporal hypergraph learning for next POI recommendation. SIGIR 2023.

[5] Luo, Y., et al. Timestamps as Prompts for Geography-Aware Location Recommendation. CIKM 2023.

[6] Feng, S., et al. Where to move next: Zero-shot generalization of llms for next poi recommendation. IEEE CAI 2024.

Meta-review: The paper introduces the Long-Tail Adjusted Next POI Prediction (LoTNext) framework to tackle the long-tail problem in next POI prediction. LoTNext features a Long-Tailed Graph Adjustment module to minimize noise and mitigate the influence of long-tailed nodes in the user-POI interaction graph, as well as a Long-Tailed Loss Adjustment module to balance the loss between head and tail POIs. Experimental results demonstrate that LoTNext outperforms several existing methods on two real-world datasets. After the rebuttal, the overall rating of the paper turned positive, indicating a leaning towards acceptance. However, some of the authors' feedback appears to be either unreasonable or inadequately addresses the reviewers' concerns (including baseline selection, scalability and the existing work about handling long-tailed issue ).
Decision: Accept (poster)