Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation
Authors: Gehua Ma
Jingyuan Zhao
Huajin Tang
Venue: ICLR 2022 Submitted (2022)
Keywords: Neuroscience
spatiotemporal aware modeling
successive POI recommendation

Abstract: POI vector representation (embedding) is the core of successive POI recommendation. However, existing approaches only rely on basic discretization and interval analyses and fail to fully exploit complicated spatiotemporal attributes of POIs. Neuroscience research has shown that the mammalian brain entorhinal-hippocampal system provides efficient graph representations for general knowledge. Moreover, entorhinal grid cells present concise spatial representations, while hippocampal place cells represent perception conjunctions effectively. Thus, the entorhinal-hippocampal system provides a novel angle for spatiotemporal aware representation, which inspires us to propose the SpatioTemporal aware Embedding framework (STE) and  apply to POIs (STEP). STEP considers two types of POI-specific representations: sequential representation and spatiotemporal conjunctive representation, learned using sparse unlabeled data based on the proposed graph-building policies. Notably, the spatiotemporal conjunctive representation represents POIs from spatial and temporal aspects jointly and precisely. Furthermore, we introduce a user privacy secure successive POI recommendation method using STEP. Experimental results on two datasets demonstrate that STEP captures POI-specific spatiotemporal information more accurately and achieves the state-of-the-art successive POI recommendation performance. Therefore, this work provides a novel solution to spatiotemporal aware representation and paves a new way for spatiotemporal modeling-related tasks.

Under review as a conference paper at ICLR 2022
SUCCESSIVE POI RECOMMENDATION VIA BRAIN- INSPIRED SPATIOTEMPORAL AWARE REPRESENTA- TION
Anonymous authors Paper under double-bling blind review
ABSTRACT
Existing approaches usually perform spatiotemporal representation in spatial and temporal dimensions, respectively. which isolates the spatial and temporal na- tures of the target and leads to sub-optimal embeddings. Neuroscience research has shown that the mammalian brain entorhinal eutortinal-hippooanpee hippocampal system provides ef- ficient graph representations for general knowledge. Moreover, entorhinal grid cells present concise spatial representations, while hippocampal place cells rep- resent perception conjunctions effecttivey. Thus, the antorhinal-hippocamee tem provides a novel angle for spatiotemporal representation, which inspires us propose the Spatio'Tempore aware Embedding framework (STE) and apply it to POIs (STEP). STEP considers two types of POI-specific representations: sequen- tial representation and spatiotemporal conjunctive representation, learned using sparse unlabeled data based on proposed graph-building policies. Notably, STEP jointly represents the spatiotemporal natures of POIs using both observa- tions and contextual information from integrated spatiotemporal dimensions by constructing spatiotemporal context graph. Furthermore, we introduce user pri- vacy secure successive POI recommendation method using STEP, and it achieves demonstrate the state-of-the-ee of-the-art performance two benchmarks. In addition, the excellent performance of the STE representation approach in other spatiotem- poral representation-centeee centerrd tasks through case study of traffic flow prediction problem. Therefore, this work provides a novel solution to spatiotemporal aware representation and paves new way for spatiotemporal modeling-relane related tasks.
sys-
INTRODUCTION
With the rapid growth of location-based web services like Instagram and Yelp, there has been seismic shift in how people interact with locations around them. Through exploitation Points-of- Interest (POIs) and their contexts, successive POI recommendation can benefit users and businesses greatly. As a core of POI information utilization, encoding POIs into vector representation space of great significance for advanced POI analysis and downstream applications. Existing studies attempt to represent POI from different perspectives and collaborate with user preference modeling usually highly correlated, naturally, sequence modeling approaches like the Markov chain model were used to capture the check-in se- quential characteristics of POIs (Ye et al.) 2011]: Liu et al., 2013 Zhang 2014: Feng et al., 2015). Employing tensor factorization technique, the works (Yang el al. 2017 Wang et al. 2018) modeled target users and POIs separately by interacted features for POI recommendation. More recently, enlightened by neural networks' success. recurrent neural nets were remolded to represent POIs and user preferences implicitly (Liu et al., 2016a) Zhao et al. 2019 Zhu et al. (017). Considering the geographical attributes of POIs, researchers have used pawer-law distribution, Gaussian distri- bution, or hierarchical tiling methods to depict the geographical influence over POI distributional
to achieve recommendation. Since consecutive check-ins
features (Ye et al. 2011 Lian al.) 2014 Feng al. 2017 Chang & Kim 2020 Luo al. 2020)
However, geographical modeling methods above only provide single-scale or coarse-grand grained man- ually designed representations of POI geographical influences, which is deficient in capturing the POI-specific spatial features. Also, the arbitrary modeling might even lead to over over-voranen parameterization While temporal dimension offers indeterminate auxiliary information for POI modeling, to utilize

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structure can be Under review as a conference paper at ICLR 2022
the POI temporal information within the check-ins, some works using time interval, time state vari- ables or temporal transition vectors to promote the POI representing (Zhao et al.) 2019 2016 2017 Li et al. 2018: Manotumruksa et al.] 2018: Zhao et al. 2020). However, these methods focused on utilizing general temporal patterns among all POIs and failed to exploit the POI-specific visiting time patterns sufficiently. Still, the POI-specific spatiotemporal characteristics were not adequately mined and utilized.
Inspirations. The entorhinal-hippocampal system plays a central role in the mammal cognition architecture. The Nobel Prize-winning neuroscience research (O'keefe & Nadel 1978) demon- strated that entorhinal grid cells provide an effective multi-scale periodic spatial representation (uaa et al. 2015: Banino et al.] 2018 Mai et al.) 2020] Dang et al., (021). Moreover, the entorhinal- hippocampal system also critical for the non-spatial inference that relies on understanding the associations between perceptions from various perspectives (Whittington et al., 2018 Stachenfeld jtt al. 2018 W Whittington et al. 2020). Some promising researches cast spatial and non-spatial prob- lems as connected graphs and point out the cells inside pntorrinal-hippocaan
structure provide efficient conjunctive representation for those graphs (Stachenteld et al.) 2018; Gustafson & Daw 2011). As the representation mechanism in the entorhinal hippocampa system extensively studied, widely accepted that conjunctions of representations from different aspects form the hippocampal representation for relational memory (Whittington et al. 2018 2020 Eichenbaum 2017 MacDonald et al. 2011, Sargolini et al.] (0066.. For the general spatiotemporal aware em- bedding, various contexts can be constructed into affinity graphs for latent representation learning. in aatorhinal-hippocane
Furthermore, strategies like
translated to improve the quality of the representations (see Fig Ieeft part).
In this paper, borrowing inspirations from the entortinal-tippoocapaal system, we propose the Spa- tioTemppral aware Embedding framework, namely STE and apply to POIs (STEP) for successive POI recommendation, the model architectures are shown in Figure Firstly, we build context graphs to enable unsupervised embedding learning on sparse check-ins. Secondly, we employ a sequential model to represent POIs from the check-in sequence perspective. Most importantly, we elaborate spatiotemporal model consists of a grid-cell spatial encoder and a visiting time encoder to capture the POI-specific spatiotemporal characteristics. The spatiotemporal model learns to get the POI spa- tiotemporal latent representations using the spatiotemporal context graph. Finally, we implement successive POI recommendation systems based on the STEP and achieve geg--performacc using simple recurrent neural networks as recommenders.
This work's main contributions summarized as follows:
(1) Motivated by the graph-representing strategy of structural knowledge in the entorhinal- hippocampal system, we solve the spatiotemporal aware embedding learning in graph-based unsu- pervised learning manner through specific context graph building policies, especially the spatiotem- poral context graph, fully exploit rich unlabeled data.
(2) Inspired by the conjunctive representation mechanism in the entorhinal-hippocaan present spatiotemporal model with a grid-cell spatial encoder and a time pattern encoder to utilize the spatiotemporal information. The conjunctive representing approach based on a unique spatiotemporal context graph addresses the problem of previous spatiotemporal modeling methods in which spatial and temporal information are isolated and represented separately.
complex,
(3) We introduce a successive POI recommendation system by incorporating STEP and simple se- quence predictors to show the feasibility of implementing specific applications based on the pro- posed STE framework. We perform experiments on large real-world datasets to demonstrate the effectiveness of STEP, and our method outperforms baselines according to experimental results.
(4) Moreover, compared with classical recommendation systems, our POI-centered solution can avoid the ethical risks of artificial intelligence, like personal data leakage, as it does not need ac- cess to private information such as user preferences. Furthermore, our framework can be applied to more valuable applications like wildlife preservation and urban traffic scheduling as general spatiotemporal aware modeling method.
2 PRELIMINARIES
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E-H system: Greph-representtnng
Conlexiual Info. Specific Info.
Context Building
Contert Graphs
Visting tmmestapp
Locations
Grid cells within E-H:
2 Self sell-posiion representingg
Sequential Model
Time Putuen Encodat
Gricooal Enooder
Place cells within E-H:
Conjunctive ropresending
Spatial Model
Temporal
Merge
Spaliolempora Model
Entorhinal-tigpooaamaal Cognitive System
Spatio Temporal Embedding framework
Figure 1: Representing mechanisms in antorhinal-hippocampal system (E-H system for short) and the framework of spatiotemporal aware embedding model. The proposed STE framework consists of three essential parts: the context graph building strategies to construct simplified affinity graphs, the spatiotemporal model extract rich item-specific spatiotemporal features, and the sequential model to extract sequential feature embeddings. The uniqueness about our spatiotemporal information usage is that we represent item from spatiotemporal perspective (not isolated) using observations and contexts conjunctively.
Table I: Notations in this paper.
Given a set of POIs with corresponding coor- dinates p {p:),2 Ps (1), check- in sequence is one set of continuous check-
Notation
Definition
j-th timestamp of p:
ins of one user in one day, denoted as Sj (I),,t),->> ..., (Pr,, t. Unlike previous works, we do not regard all check-in records of a user as one sequence since check-ins with relatively long intervals are not very relevant. Although we assign notation users for generality, the user information is not used in the training phase except to split sequences.
Visiting time pattern matrix of p;
e'
Spatial vector representation of pa Sequential vector representation of Spatiotemporal conjunctive represen- tation of p:
d'ey e't
e'step
STEP vector representation of ps Tensor concatenation operation
We define context graphs as graphs that encode context information as affinity among POIs. Various contexts in the check-in records be easily built into graphs G, (Vp. Fp), where V, is the set of POIs and Ep, is the set of edges between adjacent POIs. The edges context graphs repre- sent the correlation between neighboring POIS defined by geographical distance, relative position in check-in sequences, or spatiotemporal adjacent criterions. We summarize notations in this paper
using Table
Data description. The Instagram Check-in dataset (Chang et al. 2018) was collected from Insta- gram in New York and the data was preprocessed in the same manner as previous works (Zhao al., 2016 2017). The Instagram Check-in dataset has been pre-processed when it is made public, it in- cludes 2,216,631 check-in records at 13,187 187 POIs of 78,233 users. Check-in sequences are sorted by timestamps, the first 70% are used as a training set and the remaining 30% for validation and testing. The Gowalla dataset is globally-collected large-scale social media dataset (Cho et al. 2011). We eliminate users with fewer than ten check-ins and POIs accessed by fewer than ten users. Then the check-in records are sorted according to timestamps and first 70% check-ins are used for training and the remaining latest records for testing. We perform vivid data analyses in the Appendix due to the space constraints.
SPATIOTEMPORAL AWARE EMBEDDING MODEL OF POIS
We illustrate components of STEP: the sequential model in Sec. 3.1 the spatiotemporal model in Sec. 3.2 and state the STEP-based successive POI recommendation method in Sec. 333 We adopt simple-mindes (no maraaarmets edge weighting policy for constructing all context graphs. Weight A.; = 1 vertices and j is connected, this simplification avoids the necessity of choosing edge-weighting parameters.
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Spatial-neighboringg definition. We project the coordinates the geographical coordinate system WGS84 to the projection coordinate system NAD27 to get locations of POIs in IR? For each en- try A., in adjacency matrix of spatial context graph Gspa- we assign A using the geographical Under review as a conference paper at ICLR 2022
3.1 SEQUENTIAL MODEL
The sequential sub-model represents POIs using context graph Gaeg. Given one POI and its context in the check-in sequence, entry Aij in adjacency matrix of Gseg is 1 if pi,P? are within the same context window. This is common way to mine the sequential correlations of tokens like words (Mikolov al. 2013) POIs (Lim et al., 2020). Our sequential model aims predict true con- textual POIs, i.c. connected vertices in G Geeg. Intuitively, minimizing the objective function over all target-neighbor pairs guarantees that POIs sharing similar sequential context will have shorter distances in embedding space (Hadsell et al. (006). To avoid the intractable summation over the whole context space, we follow the noise contrastive sampling approach (Gutmann & Hyvärinen, 2012 Mikolov et al., 2013) to get an approximated surrogate loss
Leeg(0 (Oeg) =
Σ PoPjEP
[1( = 1) log aleseg eleg)) + 1(7 -1) log ol o---eey <leeg)
(I)
where 7 1 if (pt, Pj) sequential neighboring pair and y = -1 if not, indicator outputs when the argument condition is true and otherwise 0. This unsupervised loss can also be seen as taking expectation with respect to the distribution P(ps. P) over P, which is conditioned on the POI sequential context graph Geeq.
3.2 SPATIOTEMPORAL MODEL
In this section, we illustrate the POI spatiotemporal conjunctive embedding model in detail. The proposed spatiotemporal model is composed of two key components: POI spatial model and POI visiting time encoder, an intuitive illustration can be found in Fig.
3.2.1 SPATIAL MODEL
The spatial sub-model takes location observations (Ix; ys) and spatial context graph Gspa to produce spatial representations.
Grid-cell encoding. Inspired by the multi-scale periodic representation of grid cells in mammals, we formulate our POI spatial contextual encoder to use sinusoidal and cosinusoidal functions of different scales to encode the raw locations of POIs in geographical space following previous works Gao et al. (2019) Mai et al. (2020). Given POI p. (æ4, E R2, the grid-cell model based encoder encodes the coordinates in 2-D Euclidean space into spatial latent representations in Rd We denote the grid cell encoder based spatial embedding of POT p;
ésp
o (Iz v:); Ospa).
(2)
where
(2,, v1) wb (z,, k) @ J0 (z,, @ 5 (2,,
(3)
is concatenated multi-scale representations of 65 dimensions, S denotes the number of grid scales and represents fully connected non-linear layers. Considering three unit vectors [1=[1,0]²
a;=(-1/2.v3/21 v3/2
132 ./t//////.aaa//////// -V322 RR2 at each frequency, position codes
2 (Iz, v.) 18 1ig 1
(4)
are computed via
[ii Mi] as sin pdanin
Ti Mil
ax pAmin
1 ( V1) = cos(
k [1,2,3]
(5)
and p (Amax Amin) //(9-1) Amin and Amax are the minimum and maximum seale values, here use S = 64 following the previous work (Mai et al. 2020) and set Amax 1km, Amin 0.1km.
distances. Specifically, we computed the geographical distances between POIs and construct an undirected spatial context graph Gapa with uniform edges among the top-ten closest POIs (nearest neighbors policy). As the grid cell encoder can handle geographical distributions at different scales
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do not specific radius (e-neighborhoods policy) to filter the neighboring POls to fully exploit the multi-scale representation capability. The spatial graph construction pro- cess is related to Lim et al. (2020), in which the edges weighted according to average distances to enable graph attention computations.
(Mai et al. 2020),
Given a target POI Pi. neighboring contextual POI set P and negative set Pano sampled from G Gapa- the unsupervised embedding learning can simply be maximizing the log-likelihood of ob- serving the context POIs. objective function:
We can formulate this target with negative sampling via general
O(ttr) Σ Σ PLEP PjEP!
(ogo(e c(a))
+
Σ PEEP
log ooga(-a
Logo((ee'
(6)
K
where etx indicates the context graph type and cta E {seq, spa, st} in this work, a is the sigmoid function and K denotes the number of samples in negative sample set Patx- Following 6 the loss function for the spatial context embedding model is:
Sspa (0spa) (spa).
3.2.2 SPATIOTEMPORAL CONTEXT GRAPH CONSTRUCTION
00.00
02.00
0
For constructing spatiotemporal context graph Gst, want to mine the item-specific spatiotemporal conjunctions, so for each entry Ai of the adjacency matrix of Get. assign As, = following the hierarchy of neighboring timestamps + temporal neighboring spatiolemporal neghboring:
04:00
06:00
Intra day Cimerddon
Neighboring timestamps. Given an arbitrary timestamp pair (t1, time interval At & |t, tzl and Awkd & (t) wkd((2)] where (t) = if is weekend else 0. For one time f, its cemporal-neighboring timestamps are those within the neigh- borhood window and satisfy Awkd O, as shown in Fig. 2 h is a hyper-parameter indicates temporal context window width and h E (0, 24) hours.
18:00
18:00
20.00
22:00
2. POI temporal neighboring. POI p; and Pj with corre- sponding visiting timestamp Ti { ....... and T, (t/, 2....... The number of neighboring timestamp pairs (t', t))
Inter-day Dimonsion
Figure Temporal neighbor- ing examples with h 2. For 4, some times are ex- cluded from the temporal neigh- borhood window as A Awkd + O.
m, where 1' E T. 1 E T,. is threshold.
3. POI spatiotemporal neighboring. If Pt. Рj are spatial and temporal neighboring, they are spatiotemporal neighboring.
It is redundant for individual POI-specific temporal modeling since solely relying on time informa- tion, we are not able to recommend reasonable candidate POIS (visits may take place contemporarily all over the world). Thus, time is regarded as supplementary dimension of basic spatial infor- mation, our spatiotemporal context graph provides an effective way combine the POI- specific temporal and geographical information.
High
3.2.3 VISITING TIME PATTERN ENCODING
11 28
Acoumuttion
We develop a visiting time pattern encoding method to tensorise the tem- poral observations (visiting records in timestamps t) provide observa- tion imputs for spatiotemporal model. Unlike previous works (Liu et al.
2016a Zhao al. 2019 Zhu al. 2017 Zhao al. 2016), fo- cus on the POI-specific temporal patterns rather than general temporal characteristics among all timestamps. Compared with previously used time interval-based or hard-coded methods, our encoding scheme can tensorise item-specific temporal information more precisely and able to provide reliable decision basis for spatiotemporal modeling.
Augmentstion
Figure 3: Schematic of the visiting time encod- ing process.
Visiting time encoding. Since the visiting time patterns relatively
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stable the scale of year but fickle on the hour and the date scale, in encoding scheme, visits are counted by the check-in timestamps to raw matrices E 224x366, Then, the raw matrices are normalized and applied Gaussian kernel for smoothing, this process also reasonably augment the POI accessing time data. The visiting time encoding process is shown schematically in Fig. 3 The final matrix representation t conooohhnomml retains the fine-grained check-in time patterns as well as rough item-specific visiting time features.
3.2.4 SPATIOTEMPORAL CONJUNCTIVE EMBEDDING LEARNING
We formulate the spatiotemporal conjunctive representation as:
e = Φe.. (de..me (t'), espal 2 e epaa
(8)
where & indicates fully-connected layers. Follow the formulation in Eq 6 we implement the spa- tiotemporal conjunctive representation learning by minimizing:
[(Orime: Ost) O(t)).
We sample P. and Pst from Gst, where P is the set of spatiotempprral-niihhbiingg POIs whereas Pat is the set of negative POIs.
During the optimization procedure, the spatial model is jointly optimized sub-model of spa- tiotemporal model, the full objective of the spatiotemporal model
Le [(tssme Ost) + Xxpa (9
(10)
Aspa weighting factor for preserving the spatial context information during the spatiotemporal modeling. We first sample batch of spatial context Gspa to optimize the spatial context loss Lopa to preserve geographical context. Next we sample batch of spatiotemporal context Get to optimize the spatiotemporal loss Lst to preserve the spatiotemporal context. We repeat above procedures for Io and / Io/ Aapa iterations respectively to approximate the balancing factor Aspa. We update all parameters {8apa Otime 02t of spatiotemporal model in iterations until the overall loss Lat converges.
3.3 SUCCESSIVE POI RECOMMENDATION WITH STEP
We present the STEP (STE of POIs)- based recommendation method in detail in this section. Taking the spatiotemporal data input, construct context graphs G and feed the observations (loca- tions and time patterns) into the STEP model to perform embedding learning. The embeddings smoothed according corresponding context graphs preserve contextual information. Then, POI vector representations (embeddings) Eseq Est are merged as spatiotemporal aware embedding estep and fed into the recommender generate an estimated embedding é. Specifically, we use concatenation
estop = e'og + e'
(11)
to merge the two sequential and spatiotemporal embeddings. This merging policy can preserve information from different spaces without estra-parameters and not requires the embeddings to be in the same dimension (e.g. du = daeq) ) thus provides more flexibility. We adopt two-layer recurrent networks as the recommender model.
Embedding model optimizing, Parameters in the STEP embedding model are optimized according corresponding loss function C(0.) L. oi ox|001122. where L. {Cseq, 0., {0seg) Oat)
Oet {0 spa. Otime, }. a is the weighting factor of the 2-norm regularizer.
Recommender model optimizing. The predictor then optimized with the pre-trained STEP em- bedding model for the next POI recommendation task. During the training phase, given ne-length (9t)
check-in sequence Sj, we can get corresponding STEP embedding series of POIs (estep..... ..., etep ) the last POI is regarded as the recommendation target. The target of the recommender is to predict
latent representation éstep similar the embedding of true successive POI (gt) step. formally described
as:
max
Σ sim (éstepr
(gt) estep
(12)
Ppred
S,ES

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Under review as a conference paper at ICLR 2022
datasets, mark best values with bold fonts and underline the suboptimal ones. CAPE-based methods are not applicable on Gowalla (no tweets were provided) and do not report results of some methods on Instagram Check-in as they cannot be reproduced faithfully. t: with user preference consideration, $: using semantic content information.
Table 2: Comparisons with baselines
DATASET
Instagram Check-in
Gowalla
METHOD METRIC
METRID
HIT@
HIT@5
HIT @ 10
MRR
HIT@1
HIT@5
HIT @ 10
MRR
RRn
0.1197
0.2207
0.2726
0.1792
0.0715
0.0725
0.0732
0.0727
Random+LSTM
0.1207
0.2225
0.2751
0.1805
0.0722
0.0736
0.0749
0.0737
SRi
0.1356
0.2419
0.3040
0.1919
0.1090
0.2111
0.2617
0.1612
LSi
0.1318
0.2344
0.2984
0.1875
0.1085
0.2101
0.2585
0.1594
CAPE+GRU
0.1390
0.2433
0.3079
0.1953
N/A
N/A
N/A
N/A
CAPE+LSTM
0.1381
0.2412
0.3054
0.1939
N/A
N/A
N/A
N/A
Geo+GRU
0.1619
0.2616
0.3248
0.2093
0.1267
0.2309
0,2834
0.1684
Geo+LSTM
0.1622
0.2594
0.3128
0.1875
0.1233
0.2296
0.2811
0.1701
ST-RNNI
0.1054
0.2019
0.2426
0.1681
0.0519
0.0953
0.1304
0.2187
STGN
0.0256
0.0784
0.1144
0.0590
STGCN+
0.0424
0.1134
0.1625
0.0842
LSTPM
0.1261
0.2134
0.3121
0.1957
0.1468
0.2506
0.2983
0.1998
TGATT
0.1344
0.2414
0.2653
0.1856
STP-UDGAT
0.1475
0.2911
0.3285
0,2130
STEP+RNN 0.2458
0.3170
0.3502
0.2822
0.1495
0,2878
0.3634
0.2222
STEP+GRU 0.2467
0.3057
0.3336
0.2781
0.1490
0,2912
0.3636
0.2233
STEP+LSTM 0.2454
0.3204
0.3556
0.2835
0.1539
0.2968
0.3728
0.2282
The objective function of recommender is:
=
Σ
log o' (sim(e (gt) @step)) log( Σ d'(sim(c (estep @step)))
(13)
SYES
P.EP
where o' = exp(LeakyReLU()) and sim(..)) = the cosine similarity scores to rank the candidate POIs to generate recommendation lists.
During the testing phase, we compute
|a
EXPERIMENTS
Table 3: Context graphs statistics
two POI datasets.
We perform the successive POI recommendation task on the Instagram Check-in dataset. Gowalla dataset, and the traffic flow forecasting task on TaxiBJ15 and TaxiBJ.
AVERAGE VALUE
Ins.
Gow.
Records per POI
168.1
34.2
Eseq per POI
66.2
35.5
Expo per POI
10.0
10.0
Estt per POI
0.997
0.596
4.1 SUCCESSIVE POI RECOMMENDATION TASK
Metrics. During the system inferencing phase, the recommendation system recommends POI list according to the estimated scores of candidate POIs for every trial sequence. We apply widely-used metrics HIT@K K (if the ground truth within the top-k of the list, a score of is awarded, else 0), k = 1, 1,5, 10 and MRR (Mean Reciprocal Rank) for evaluation. These metrics reflect different aspects of the recommendation lists, HIT @ measures the rate of valid recommendation among all trials, whereas MRR scores the quality of the entire recommendation list.
Hyper-parameter settings. We set the hyper-parameters of our proposed method to the following default values. We set context window size in the POI sequential model to 2 and adopt h = 2. m 11 for building G.t. We utilize Adam optimizer with batch size 512, ,, 33 0.9, /32 0.999 and set the initial learning rate 0.001 followed by a reduce-on-plateau decay policy, the decay factor 0.1 during the training. Weighting factors a, Xepa are set to x 10-4 0.2 and the embedding dimensions (dsayy depay d are set {32, 64, 96).
We compare the STEP-based successive POI recommendation method with representative methods: methods. We choose six methods consisting of three embedding mod- els and two recommenders. For the embedding model. we use the following embedding models. (1) Random, (2) Skip-Gram (Liu al. 2016b), (3) CAPE (Chang al. 2018) and (4) Geo (Mai
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2
wes
1088 1i8l
8888
8888
Figure 4:
h, m, Aspa on Instagram Check-in and Gowalla datasets. The means and standard deviations computed over five runs using different random seeds.
Effect of
et al. 2020p. For the recommender model, two-layer networks based on (1) GRU unit (Merri & Fellow 2014) and (2) LSTM unit (Hochreiter Schmidhuber] 1997) are used. One-stage meth- ods. We choose representative one-stage methods as baselines. (1) ST-RNN (Liu et al., (01 (2) STGN (Zhao et al., 2019), a LSTM variant which models visit preferences with time and distance considerations, and the improved variant STGCN. (3) LSTPM (Sun et al.) 2020) is LSTM-based method. (4) STP-DGAT and STP-UDGAT (Lim et al. 2020) are spatial- spaatall-emmporrpppeeeeeeeee user
dimensional graph attention networks.
Comparison results. According to results in Table 5 our method outperforms the baselines by significant margins both datasets, and the gains in recommendation accuracy especially sub- stantial on the Instagram dataset with rich temporal information (according to Table 3 POIs in Gowalla have less visiting timestamps). The one-stage recurrent tatwork-basee methods, such as ST-RNN, LSTPM surpassing basic embedding- preference spatiotemporal properties. However, these methods remain noncompetitive with STEP- based ones, although the STEP stands without user preference consideration. The advantageous per- formance of our method over the competitors can attributed to its efficient use of the item- sppccici spatiotemporal nature. We observe significant performance improvements of the STEP-based meth- ods in terms of MRR, indicating the STEP-based methods provide better candidates lists on both datasets, benefit from the efficiency of the proposed spatiotemporal aware embedding model. The usage of LSTM units in recommender slightly improves the performance compared with basic RNN cells because of their advantages in gate functions of recurrent connections. Moreover, the basic RNN recurrent net equipped with STEP embedding can also outperform one-stage SOTAs. This also proves the effectiveness of our brain-inspired spatiotemporal aware embedding model.
methods by more sufficient exploitation of user-
Effect of syyper-paramtery We study the effect of and report the results using HIT@ 1 and MRR in Fig 4 h and control the sparsity of the spatiotem- poral context graph and Aspa regulates the importance of the spatial context smoothness term in the spatiotemporal model objective function. We alter to build spatiotemporal context graphs with de- creasing sparsity as larger h corresponds to coarse temporal--eighhoring condition. The increasing within certain range results in performance improvements but appears detrimental the precise top-1 recommendation. The best performance (determined by MRR) is obtained when h = 2. The choice of h task-related, according to our results on two dataset, h 2 can be good initial value for POI recommendation according to our results. This value can be further adjusted for different application scenarios or datasets build spatiotemporal context graphs with desired sparsity. We set from to 15, larger m leads to sparser spatiotemporal context graph. We observe the performance slightly changes after increasing the threshold m, when m 11, the best performance is obtained. We also investigate the effecc of the balancing factor Aspa for spatiotemporal model training, the recommendation system achieves the best performance when Aapa = 0.2 and further increases only bring minor improvements, thus select 0.2 as default value in this work, this also helps reduce unnecessary iterations during the model training.
dyper-parameteen h, m, Aspas
We study the effectiveness of STEP modules by performing successive POI rec- ommendation task with LST recommender, the method using standard STEP embedding model is referred as FULL.
Ablation study.
After the removal of (A) POI-specific time information processing module, the spatiotemporal model degenerates to spatial model. According to Table 4 the method performs worse with- out (A) on both datasets. According to Table 3] POIs in Gowalla have sparser specific obser- vation is and contextual information from Gat. This results more significant performance
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Query
Top 5 retrieved POIs
gam STEP
radionn, famoue seaload spot
vidlows, la landrurt & Natorical
reidoon hods manaes
vidiown public vanaportiian
krdtown sandwichee.
natonncl buildinge
middown tandreeria
Choyker Buildig
CAPE Gvo 94%
krons, pare
Metropolitnn
ridtown, cocksail bars
Bon
upper runitattan park
Loftus Playground
roosevall island, park
Rocsovell Islend Sports Pork
apper east site, artglllrry
Uni Gollery
Grand Houz New York
B blotcco
Carcccol
Tudor City Conena
Mlcmossn RamondButt
uvent spanes
light leanges
Inarges
reatown, parks
plass
lepes
hele Lobster
Hunilnngton Harbor
1- Vieuud reddoonn food
interooninnnttiiiio N.V. Banday
Grand Central Dyoter Bar
vndiown
farber
Figure 5: Retrieval results on Instagram dataset Top 5 candidates are placed from left to right. Green and red flag marks represent positive and negative POIs determined by both opening hours and geographical position. Under names of those POIs, we list their region and representative tags collected from websites with bold fonts.
improvements on the Instagram set than Gowalla. We replace POI visiting time pattern ma- trices t (applying Sec 3.2.3) with -aitoi-intiialized matrices in I2 366 We note that the use of (B) POI visiting time encoder improves the recommen- dation performance on both datasets, and the im- provement are positively correlated with the tempo- ral information abundance of the dataset. We use one-layer neural network location encoder v' y)
Table 4: Effectiveness of using (A) tempo- ral information, (B) visiting time encoder, (C) grid-cell encoder, (D) spatial embedding preserving and (E) the spatiotemporal model.
We mark the best ones with bold fonts and unden underline the suboptimal ones.
HIT@ HIT @ HIT @ 10 MRR
FULL 0.2454 0.3204
0.3556
0.2835
to replace (C) grid-cell encoder in the STEP to
Instagram
w/o (A) 0.2433 0.2544
0.2607
0.2504
demonstrate its effectiveness. Results in Table 4
w/O (B) 0.2452 0.3151
0.3442
0.2798
demonstrate that the grid-cell encoder improves the
w/o (C) 0.2399 0.3037
0.3305
0.2727
w/o (D) 0.2298 0.2531
0.2674
0,2451
quality of STEP representation and leads to better successive POI recommendation performances on both datasets. We observe noticeable performance drops after the removal of (D) spatial embedding preserving in Table since the spatiotemporal con- junctive representation integrate spatial and tempo- ral attributes at the cost of spatial information loss (due to the dimensional reduction). The use of (D)
only (E) 0,2466 0.2840
0.3021
0.2664
FULL 0.1539 0.2968
0.3728
0.2282
Gowalla
w/O (A) 0.1461 0.2825
0.3540
0.2174
W/O (B) 0.1509 0.2921
0.3664
0.2248
w/O (C) 0.1006 0.2169
0.2922
0.1657
w/O (D) 0.0973 0.2117
0.2866
0.1610
only (E) 0.1252 0.2142
0.2519
0.1683
spatial embedding information preserving alleviate this problem to certain extent.
A simple LSTM recommender can achieve competitive recommendation performance without check-in sequential information consideration (Table 4 only(E)), demonstrating the effectiveness of the spatiotemporal model in STEP. As visiting sequential information provides relatively coarse- grained POI depictions, the removal of the sequential model even leads HIT@1 improvement on the Instagram dataset. Also, the metric fallen on Gowalla (-18.6% -27.8%, -32.4%, 1224%%.-26.2. -26.2%) is more significant than those on Instagram set (+3.1% 9,,, -11.4% -150%% -6.0%). exactly opposed to their temporal information abundance, quantitatively evaluated by average timestamps per POI.
4.2 PERFORMING TRAFFIC FLOW FORECASTING TASK WITH STE
To demonstrate the generalizability of STE, perform traffic flow forecasting task with the proposed spatiotemporal embedding methods. The relevant content is presented in detail in Ap- p.ndix.A.
5 CONCLUSION AND DISCUSSION
In this paper, we propose the spatiotemporal aware embedding framework STE and apply it to POIs (STEP). To the best of our knowledge, this is the first work that translating paarhinal-iippocampp representing mechanisms to the spatiotemporal embedding. Inspired by the graph-representing pol- icy in brain entorhinal- hippocammpll system, STEP captures sequential and spatiotemporal repre- sentation from unlabeled sparse data through context graph building and graph- based embedding learning. Moreover, STEP provides highly efficient spatiotemporal model motivated by grid cells' multi-scale spatial representation and place cells' conjunctive representation, which overcomes the problem caused by frequently used separate-represeentnn. STEP-based successive POI recommen- dation method outperforms baselines and SOTAS on two real datasets without user preference in- vasion. Furthermore, this work presents a practical framework for effective spatiotemporal aware modeling of general items, enabling more valuable spatiotemporal-related tasks such as wildlife preservation and urban traffic management.
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A CASE STUDY: TRAFFIC FLOW FORECASTING WITH STE
As an essential basis of traffic scheduling, accurate traffic flow forecasting is of great significance. In this part, we perform traffic flow forecasting with proposed STE, which abbreviated as STE-TG (STE of Traffic Grid).
A.1 PROBLEM FORMULATION
(a)
(b)
3m,m k
(0,0)
( n)
mut
k
t= k
Figure An illustration of (a) grid map partition and (b) flow a grid.
Following previous works (Hoang et al. 2016; Zhang al. 2016), we partition city into an M x N traffic grid map based on longitude and latitude where traffic grid represents a geographical region. For grid (m, n) that lies at the m row and the n column, two types of traffic flows at hthh timestamp are considered, namely inflow and outflow, defined as
inmm. I
Σ TreeP
His 1 9i-1 $ (m, n) A gr (m,
(14)
out M,
Σ TrXEP
1i22 E (m, 0041 & n),
(15)
k
where Trk means the trajectory the tth timestamps, gi indicates the geographical coordinate, E (m, n) means point 9, lies within grid (m, n) and means the cardinality of set. At mot,,,.)) The
timestamp k, the traffic flow of grid (m, n) is represented as X.m. (x* (n....m T goad of traffic flow forecasting is: given X E (0, I,.... - 13. predict X,.
A.2 METHODOLOGY
As illustrated in Fig.1 I in the main text, the input of STE-TG is composed of observations (coordi- nates and visiting time patterns) and contexts (context graphs). Unlike STEP, there is no sequential traffic information, the sequential model is not applicable.
Following Sec.3.2.1 in the main body of the article, we use spatial observation and build a spatial context graph in the same way. Specifically, same hyper-parameter settings of S, Amnnmaaz is adopted in grid-cell encoder, and top-10 policy is used for building Gspa- Also, the embedding objective of the spatial sub-model is still the term Eq.7. Different from POIs, the timestamps of the traffic grid flow data are collected using standard time interval. Thus both the encoding of visiting time patterns and the construction of spatiotemporal context graph could be simplified. Notably, generate the visiting time pattern matrix the temporal observation input for the spatiotemporal model in the same way we did for POIs, and since the timestamp is standard, skipped the accumulation step in Fig.3. For constructing Gat, we define the grid temporal ad- jacency using specified distance function d(..-) which is calculated by the normalized traffic flow difference value of two traffic grids.
STE usage
d((m, n), (m', n2)) A t Am
abs inmm.
mm.mm 2
out,m,n _out,mm pput.mm.nn)]]
) (16)
+ abs
2
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Ground Truth Inflow
Ground Truth Outflow
150
125
100
100
75
75
50
50
25
-25
10
14
16
10
16 16
Predicted Inflow
Predicted Outflow
200
200
175
175
150
125
50
8
Figure 7: Visualization of predicted results and ground truth traffic flow TaxiBJ dataset. Figures in the top row are ground truth flows and the bottom ones are the predicted values. STE-TG can achieve accurate traffic flow prediction only using traffic information the previous time step and the spatiotemporal representations extetg of the center grid and the adjacent grids.
We use the top-10 10 strategy to get the temporal neighboring relations of grids with low distances. To- gether with the spatial context information G Sepa Get is built to help the embedding model preserve the spatiotemporal conjunctions between the traffic grids. The objective function used to optimize the spatiotemporal model remains the same as in the main text.
Predictor Design The flow of one grid is significantly related to the nature of the grids around because an outflow from one region can lead an inflow to neighboring regions. In this work, the spatiotemporal natures of grids and correlations between grids are encoded in the embedding vector estetg- Thus, Given a query X.m. spatiotemporal embeddings of four geographical closest grids and grid (m, n) itself will also be fed into predictor fo to produce the estimation
Xm m. (# (. 12 mn
fe( m. e..))
(17)
where e... (estetgr eseey and egtefy indicates embeddings of four spatial neighbors of grid (m, n). We use simple three-layer MLP so with ReLU activations for implementation, the dimen- sion of hidden state is 256. The loss function used is mean squared error | X113.
A.3 EXPERIMENTS
Following the same pipeline we used in POI recommendation task, we first train the spatiotemporal embedding model STE-TG and then optimize the recommender model on the training set. All models are optimized using Adam optimizer with default Bs, the learning rate is set to 1 x 10-3 The embedding dimensions dst) are {64,960 96).
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Table 5: Comparisons with baselines TaxiBJ15 dataset.
METHOD
RMSE
ARIMA
25.58
SARIMA
29.11
VAR
25.59
CNN
26.08
DeepST-CPTM STE-TG (Ours)
22.59
21.92
Table Comparisons with baselines on TaxiBJ dataset.
METHOD
RMSE
HA
57.69
ARIMA
22.78
SARIMA
26.88
LinUOTD
21.23
ConvLSTM
19.54
DeepST-CPTM STE-TG (Ours)
18.18
18.03
Metrics
We measure the traffic flow forecasting performance by Root Mean Square Error (RMSE)
as
RMSE
Σ (z. 2,)2
(18)
where a and æ are the predicted value and ground truth, respectively and t is the number of all queries.
Datasets
We perform experiments on datasets:
TaxiBJ15 (Zhang al. 2016) consists of calculated inflow/outloww based on taxicab GPS data collected in Beijing city in 2015.
TaxiBJ (Zhang et al. 2017) is an extended version including data from 2013 to 2016.
For TaxiBJ15, data from the last week is used for testing. For TaxiBJ, we choose the last four weeks as the testing data.
Baselines
We choose representative baselines for comparison:
HA: a predict method by the average value of historical values.
ARIMA (Auto-Regressive Integrated Moving Average)
SARIMA: seasonal ARIMA
VAR (Vector Auto-Regressive)
ST-ANN: It first extracts spatial (nearby 8 regions' values) and temporal (8 previous time intervals) features, then fed into an ANN.
DeepST (Zhang et al,, 2016): a deep neural network (DNN)-based prediction model for spatiotemporal data, which shows state-of-the-art results crowd flows prediction, only consider the most potent variant, i.c. DeepST-CPTM.
ST-ResNet (Zhang et al., 2017) ST-ResNet models citywide traffic flow at different times into 2D images perform prediction.
LinUOTD (Tong et ization.
2017): linear regression method with spatio-temporal regular-
ConvLSTM (Xingjaan al. 2015 LSTM variant with convolution modules.
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Results We listed the comparison results in Table 5 and T In STE-TG, only the grid map and historical traffic flow are used to perform flow prediction, while existing works usually take meta-data like weather into account. However, observe that the hastily constructed traffic flow prediction methods STE-TG achieve competitive performance, which also demonstrates the effec- tiveness of the proposed STE framework.
16


Reviews:
[{"review_id": "zY0OVuDJUW", "reviewer": "Reviewer_a1wE", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "Strengths:\nThe paper proposed a new model to utilize both spatial and temporal information of POIs to make Successive POI Recommendations.\n\nWeaknesses:\n1. The paper claims the proposed solution is based on the entorhinal-hippocampal system to process spatial and temporal information. However, spatial and temporal information has long been applied for POI embedding and recommendations. It seems the major difference between the proposed model has the theoretical support from entorhinal-hippocampal systems while others have not, but I cannot find such proof in the paper.\n\n2. It's not clear to find a clear proof or discussion regarding the difference with other models in the use of spatial and temporal information.\n\n3. The writing of Section 3 needs more clarification. It's unclear if the STEP model in Section 3 is learned per sequence, per user, or all records together. The description in Section 3.1 says \"Given one POI and its context in the check-in sequence\", my understanding is:\nThe sequential model (Sec. 3.1) G_seq is learned for each sequence, which refers to \"one set of continuous checkins of one user in one day\". \nThe spatial model (Sec. 3.2) G_spa is learned for all POIs regarding their coordinates.\nThe temporal model (Sec. 3.3) is learned from all visits (from different sequences) to each POI.\nThe spatiotemporal neighboring considers both spatial model and temporal model. Thus, the recommendation highly prefers POIs in the top K closest neighbors which were also visited frequently by all users within a short time window.", "summary_of_the_paper": "The paper proposed SpatioTemporal aware Embedding framework for POIs (STEP), which considers two types of POI-specific representations: sequential representation and spatiotemporal conjunctive representation. Specifically, the spatiotemporal conjunctive representation represents POIs from spatial and temporal aspects jointly.", "summary_of_the_review": "The paper needs to clarify the model definition and how each sub-model is integrated.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "okgYcZgSqP", "reviewer": "Reviewer_sU9T", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "The introduced spatiotemporal model of STE, which consists of a grid-cell spatial encoder and a visiting time encoder, is capable of mining the POI-specific spatiotemporal characteristics. Finally, the authors implement successive POI recommendation systems based on the STEP using simple recurrent neural networks as recommenders. Experimental results on Instagram Check-in and Gowalla datasets demonstrate that STEP achieves the state-of-the-art successive POI recommendation performance. However, the novelty of this paper may be over-claimed. My detailed comments are as follows.\n\nPositive points:\n1.\tThe proposed conjunctive representing approach based on a unique spatiotemporal context graph solves the problem of previous spatiotemporal modeling methods in which spatial and temporal information are isolated and represented separately. \n2.\tThe proposed method does not need access to private information such as user preferences. \n3.\tThe experimental results on Instagram Check-in and Gowalla datasets demonstrate the proposed method outperforms baselines.\n\nNegative points: \n1.\tThe motivation and novelty of the proposed method are not convincing.\n1)\tIn the first contribution, the authors claim that they propose this method motivated by the graph-embedding strategy of structural knowledge in the entorhinal-hippocampal system, which is very confusing. The proposed sequential context graph $G_{seq}$ and spatial context graph $G_{spa}$ are very similar to temporal graph $G_t$ and spatial graph $G_s$ in STP-UDGAT[1], respectively. However, the authors do not explain anything about this.\n2)\tThe authors design the grid-cell spatial encoder motivated by grid-cells in the entorhinal-hippocampal system. However, it is worth noting that the formulations of the grid-cell spatial encoder are the same as Space2Vec [2]. The authors only slightly claim that the number of grid scales following the previous work [2].\n3)\tAccording to [3], hippocampal place cells encode a geometric representation of space. It is very confusing to claim that hippocampal place cells represent perception conjunctions effectively in the abstract. As shown in Figure 1, why do the authors propose the spatiotemporal conjunctive representation motivated by place cells? More explanations on it may be better.\n\n2.\tThe authors claim that POI $p_i$ and $p_j$ are spatiotemporal neighboring if they are spatial and temporal neighboring in Sec.3.2.2. However, why do average values of $E_{st}$ per POI exceed those of $E_{spa}$ per POI in Table 3?\n\nMinor issues:\n\n1.\tIn the Introduction, \"Most importantly, we elaborate … an visiting time encoder …\" should be \"Most importantly, we elaborate … a visiting time encoder …\".\n\n2.\tIn the One-stage methods of Sec.4, “an LSTM variant” and “an LSTM-based method” should be “a LSTM variant” and “a LSTM-based method”, respectively.\n\nReferences:\n\n[1]\tSTP-UDGAT : Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation. In Proceedings of the ACM International Conference on Information & Knowledge Management, pp. 845–854, 2020.  \n[2]\tMulti-Scale Representation Learning for Spatial Feature Distributions using Grid Cells. In Proceedings of the International Conference on Learning Representations, 2020.  \n[3]\tThe hippocampus as a predictive map. Nature Neuroscience, 20(11):1643–1653, 2018.  \n", "summary_of_the_paper": "This paper proposes a SpatioTemporal aware Embedding framework (STE) and applies to POIs (STEP) for successive POI recommendation. Specifically, the proposed STE framework consists of three parts: the context graph building strategies to construct simplified affinity graphs, the spatiotemporal model to extract the spatiotemporal features of check-ins, and the sequential model to extract sequential feature of check-ins. ", "summary_of_the_review": "The introduced spatiotemporal model of STE, which consists of a grid-cell spatial encoder and a visiting time encoder, is capable of mining the POI-specific spatiotemporal characteristics. Finally, the authors implement successive POI recommendation systems based on the STEP using simple recurrent neural networks as recommenders. Experimental results on Instagram Check-in and Gowalla datasets demonstrate that STEP achieves the state-of-the-art successive POI recommendation performance. However, the novelty of this paper seems to be over-claimed. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "kQQv6LR48Km", "reviewer": "Reviewer_F7HP", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "This paper proposes a spatiotemporal-aware embedding framework and apply to the successive POI recommendation problem. The proposed method, called STEP, is inspired by the mammalian brain entorhinal-hippocampal system that has the conjunctive representation mechanism. Thus, the main motivation of the STEP is to learn the conjunctive representations on the unique spatiotemporal context graphs, which is different from the previous methods dealing with the spatial and temporal information separately.\n\nThe experiments are conducted on two real-world datasets, and the experimental results show that the proposed STEP could significantly outperform other baselines including embedding-recommender methods and one-stage methods, although the STEP does not utilize the user preference information.\n\nOverall, the proposed visiting time pattern encoding and spatiotemporal conjunctive embedding learning are interesting and useful, and the paper is well written. The concerns are list below:\n\n1.\tFor the neighboring timestamps, it is unclear why the weekend time is not be considered.\n2.\tThe paper claims that the proposed framework could be applied to other applications such as the wildlife preservation and urban traffic scheduling problems. However, it is better to give some more details about how to transfer the proposed method to other tasks. For example, the authors could provide a case study experiment on traffic scheduling problem.\n", "summary_of_the_paper": "To address the successive POI recommendation problem, this paper proposes a brain-inspired spatiotemporal-aware embedding method, called STEP, which learns the conjunctive representations over the unique spatiotemporal context graphs. The experimental results demonstrate the superiority against other SOTA methods", "summary_of_the_review": "This paper is technically sound, and the experiments are sufficient. Thus, my rating for this paper is weak accept. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "54moBcrQjkp", "reviewer": "Reviewer_qsPw", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "main_review": "Strengths\n1. The writing is easy to follow.\n2. The inspiration from the entorhinal-hippocampal system is interesting.\n\nWeakness\n1. In the experiment, how to construct user preference embedding for baselines is not clear.\n2. What is the difference of the objective between the sequential model and time pattern model? It seems the sequential model can also capture temporal information.", "summary_of_the_paper": "This paper proposed a successive POI recommendation method inspired by entorhinal-hippocampal system.", "summary_of_the_review": "The inspiration is interesting. But some parts need further clarification. I suggest \"weak reject\".", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: Despite some positive points, the criticisms (and overall scores) put this paper below the bar. The reviewers raise issues of novelty, as well as problems with the experiments and argue that some claims are unsupported.
Decision: Reject