SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Authors: Yinghui Liu
Hao Miao
Guojiang Shen
Yan Zhao
Xiangjie Kong
Ivan Lee
Venue: NeurIPS 2025 poster ()
Keywords: Out-of-town trip recommendation; Static-dynamic user preference; Ordinary differential equation
Primary area: deep_learning

Abstract: Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such user preferences. Meanwhile, existing methods often conflate the static and dynamic preferences, resulting in suboptimal performance. In this paper, we for the first time systematically study the problem of out-of-town trip recommendation. A novel framework SPOT-Trip is proposed to explicitly learns the dual static-dynamic user preferences. Specifically, to handle scarce data, we construct a POI attribute knowledge graph to enrich the semantic modeling of users’ hometown and out-of-town check-ins, enabling the static preference modeling through attribute relation-aware aggregation. Then, we employ neural ordinary differential equations (ODEs) to capture the continuous evolution of latent dynamic user preferences and innovatively combine a temporal point process to describe the instantaneous probability of each preference behavior. Further, a static-dynamic fusion module is proposed to merge the learned static and dynamic user preferences. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions, showing that SPOT-Trip achieves performance improvement by up to 17.01%.

SPOT-Trips Dual-Preference Driven Out-of-Town Trip Recommendation
Yinghui Liu'; Hao Miao?; M Guojiang Shen', Yan Zhao?, Xiangjie Kong' Ivan Lee* *Zhejiang Key Laboratory of Visual Information Intelligent Processing, College Computer Science and Technology. Zhejiang University of Technology, Hangzhou, China Department of Computing. Hong Kong Polytechnic University, Hong Kong, China Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China STEM, University of South Australia, Adelaide, Australia (2112112249, gjshen1975)e2jut. edu. cn, hao haw.miao0poll... mim.@polyu.....mmoo wdw.yu..uuu..... edu. hk, zhaoyan@ueste edu, en, xjkong@ieee org, Ivan LeeBunisa edu
Abstract
Out-of-town trip recommendation aims to generate sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based personalized itineraries, e-g., origin, destination, and trip duration. Mod- eling the complex user preferences-which often exhibit two-fold nature of static and dynamic interests-is critical for effective recommendations. However, the sparsity of out-of town check-in data presents significant challenges in capturing such user preferences. Meanwhile, existing methods often conflate the static and dynamic preferences, resulting in suboptimal performance. In this paper, we for the first time systematically study the problem out-of-townn trip recommendation. A novel framework SPOT-Trip is proposed to explicitly learns the dual static- dynamic user preferences. Specifically,
handle scarce data, we construct a POI attribute knowledge graph to enrich the semantic modeling of users' hometown and out-of-town check-ins, enabling the static preference modeling through attribute relation-aware aggregation. Then, we employ neural ordinary differential equations (ODEs) to capture the continuous evolution of latent dynamic user preferences and innovatively combine temporal point process describe the instantaneous probability of each preference behavior. Further, a static-dynamic fusion module is proposed to merge the learned static and dynamic user preferences. Extensive ex- periments on real data offer insight into the effectiveness of the proposed solutions, showing that SPOT-Trip achieves performance improvement by up to 17.01%
Introduction
With the proliferation of location-based social networks (LBSNs), location-based recommendation has become an important means help people discover attractive and interesting points of interest
(POIs) [10 (2). including POI recommendation [52 (22). trip recommendation [14] [4, 39], and out-
of-town recommendation [46] 47 32] Traditional POI and trip recommender systems dedicated
to recommending POIs within a specific region. However, they may fail when users travel out of their hometown [7]. Consequently, out-of-town recommendation [32] emerges, which generates POIs for users traveling from their hometowns to regions that they have seldom visited previously.
"Equal contribution. I Corresponding author. 'The code of SPOT-Trip can be found at http://ghhhaaccmm//iiiiiiiiiiiii
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Existing out- recommendation methods often focus on addressing the problem of interest drift 43 32 and geographical gap [25 [2]. However, these methods are mainly designed for the next POI recommendation (see the left part of Fig. T while lacking the capabilities to provide comprehensive trip itinerary for travelers. In real-world scenarios, given an origin and destination, users may prefer a model that can generate sequence of intermediate activities (see the right part of Fig. to realize a more engaging journey for convenience. To achieve this, we for the first time study new problem, i.e., out-of-town trip recommendation, that can provide consecutive intermediate POIs given the origin, destination, and the number of stops. Such trip recommendation models are expected to enable systematic itinerary generation, facilitating efficient decision makinn for the users.
However, data sparsity poses great challenge for out-of-town trip recommendations since users often have few or even no historical check-in records in out-of-town regions. It is hard to obtain a well-performed out-of-town trip recommender model with such scarce data. In addition, it is challenging to learn the complex user preferences to alle- viate interest drifts (i.e., out-of-town check-ins are not aligned with home- town check-in preferences) for ef-
Travel
A user's hometown check-ins
Out-of-town check-ins
GYM
HOTEL
NUUSUN
PUD
808
Fetch
Fetch
fective out-of-town trip recommenda- tion [6 47). Intuitively, the user pref- erences can be categorized into two complementary components: static (or invariant) and dynamic prefer- ences. On the one hand, static pref- erence captures long-term user inter- ests and stable behavioral tendencies, which are often extracted from home- town check-in records. Although static preference shows the stable ten- dencies of user. directly applying to out-of-town trip recommendation may be suboptimal due to cold-start and data scarcity. To enable better generalization, it calls for an alignment method effectively transfer sufficient knowledge learned from the hometown, i.e., static preferences, to the target regions. On the other hand, dynamic preference reflects the short-term behavioral patterns that are sensitive to contextual information, e.g.. time, location, and intent.
Query
Out-of-town Recommender
Out-of-town Trip Recommender
Intermediate FOI(S)
Next POI
000
Figure 1: Comparison of two out-of- town recommendation tasks. The blue circles indicate the user's 's hometown histori- cal check-ins, green circles represent the given query POIs, i.e., the origin and destination of trip, while the hollow dashed circles denote the intermediate POI(s) to be inferred.
often learn the entangled user preferences. We argue that effective disentanglement of the two preferences can facilitate user intent modeling across diverse scenarios and explicitly mitigate the interest drift, thereby enabling personalized recommendation and improving the robustness and effectiveness. Further, it is challenging to fuse the static and dynamic preferences, which enhances trip recommendation by taking preference consistency and personalization into account, simultancously. enabling more accurate and context-aware user model- ing. To this end, we propose Static-dynamic Preference aware Out-of-Town Trip recommendation framework, SPÖT-Trip, to explicitly learn such dual user preferences. SPOT-Trip encompasses three major modules: Enowledge-enhanced static preference modeling, ODE-based dynamic preference learning, and static-dynamic preference fusion.
Nonetheless, existing methods
42 32
The core idea of SPOT-Trip lies in jointly modeling sequence-level static preferences with semantics- enhanced representations and POI-level dynamic preferences by an ODE. First, we propose a knowledge-enhanee static preference learning module. In particular, we construct a POI attribute knowledge graph based on user check-ins to incorporate rich semantic relations, e.g., hasRating relation between POIs and attribute entities such as 5-star. Next, a relation- aware attention aggregation mechanism is designed to generate enriched POI embeddings, which capture the entity semantics with diverse relational contexts, alleviating the data sparsity. Further, we propose novel statie preference alignment mechanism to transfer knowledge of the static preferences learned from hometown check- ins to the out-of-town region. The static preferences are learned by a static aggregator, which aggregates the enriched POI embeddings at the sequence level. Second, we develop an ODE-based dynamic preference learning module to model the continuous and irregular evolution based on the dynamic user behaviors during out-of-town trips. A temporal point process [21 is further employed to characterize the probability of preference behaviors over time, enabling dynamic preference

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inference. Notably, we incorporate auxiliary geographical coordinate information to refine the behavior representation. Finally, a static-dynamic preference fusion module is proposed to fuse the learned static and dynamic user preferences for effective out-of-town trip recommendation.
The major contributions of the work is the first systematic study to learn out-of-town trip recommendation. We propose a framework called SPOT-Trip to explicitly capture the static and dynamic user preferences for effective out-of-town trip recommendation. (2) Novel Techniques. An innovative static preference learning module is proposed, which leverages sufficient semantic knowledge to extract stable user preferences. We extract the dynamic user preference based on neural ODE in conjunction with a novel temporal point process, which can capture the continuous preference drift. (3) Superior Performance. We report on extensive experiments using real data, offering evidence of the effectiveness of the proposals.
follows: (1) New Task. To the best of our knowledge, this
2 Preliminary
Definition 1: (POI Attribute Knowledge Graph). The POI attribute knowledge graph is defined as Gb (u, r, e), which encodes a semantic relation between POI v and an entity It captures external knowledge by incorporating diverse types of attribute entities and their relationships with POIs, such as (Balboa Park, Located in, San Diego).
Definition 2: (Check-in). user check-in is denoted as a tuple c = 1,,,,,,),, indicating that user u visited POI time and location where includes the geographic coordinates, i.e., latitude and longitude.
Definition 3: (Out-of-town Travel Behavior). Given a user the out-of-town travel behavior is denoted as & = (u, C22 Co ap ao). indicating that u departs from his/her hometown ag to visit an out-of-town region a, with check-in records in both regions, i.c., c, (hometown) and t (out-of-town), respectively. We use M = [h and N 1co to represent the number of check-ins in the hometown and out-of-town regions, respectively, where M N.
Problem Statement: (Out-of-town Trip Recommendation). Given set of users 4 (14),1)
i=1 POIs V = [ regions AA = {a,)]=) and out-of-town trip records O = { our objective learn recommender function F based on historical records O and the POI attribute knowledge graph Gr. For a user u² of Ll at aj with hometown check-ins T₁2 and out-of-town origin- trip query Q = {ng, ng, N) region a where rg, rg. and N denote the start, end points and the number of stops, the learned F generates a sequence of POIs [ef, ug..... ( J. where of = 0g and oy r for u². The recommended POIs in r are in a"
3 Methodology
We proceed detail the dual preference-aware out-of-town trip recommendation framework, SPOT- Trip. We first give an overview of the framework and then provide specifics. As illustrated in Fig. 2 SPOT-Trip encompasses three major modules: wwwwledge-Enhhneee Static Preference Learning (KSPL), ODE-Based Dynamic Preference Learning (ODPL), and Static-Dynamic Preference Fusion.
KSPL aims enhance the static semantic awareness of user preference by means of knowledge graph construction, which consists of two components. First, semantic knowledge aggregation is proposed improve POI embeddings with a relational attention mechanism based on the constructed POI attribute knowledge graph. Second, we design an innovative static preference alignment component that facilitates the transfer of static hometown preferences to the out-of-town region. ODPL is dedicated to capturing the evolving nature of user preferences with novel ODE-based continuous dynamies modeling. In this module, we first aggregate the hometown check-in sequence with spatiotemporal information and feed the resulting sequence into Transformer to generate initial latent representations, which are then input into a neural ODE to capture dynamics. Subsequently, the check-in behaviors are further interpreted by temporal point process to characterize the instantaneous probability of each behavior. Finally, we propose a static-dynamie preference fusion module to merge the learned static and dynamic user preferences.
3

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POI Attribute
Knowledge Graph
Semantic Knowledge Aggrogation
Static Preference Alignment
9K
(e, Te v7
Static Preference Aggregation
Relation
W
+
Mean Aggregator
+
LeakyReLU
Softmax
ML.P
PPOls
Entitiesl
+LENN (e, Vep.
Minimize the Euclidean Distance
Ls
Knowledge Enhaneee Static Preference Learning
Hometown embedding
Query
Transformer
P
Out-of-town embedding
Head
Prediction
Logit
Out-of-town
Q
Trip Recommender
Entity embedding
W
CE
Hometown & Out-ol town Check-ins
Preference embedding
Position
po
R
Static-Dynamic Preference Fusion
DDDE-Based Dynamic Preference Learning
t
Maximize
Point Process 7() +
LD
the ELBO
10
p
E(-) vo
v"
Neural ODE
lig
ltat
Sample
Dynamic Spatiotemporal Behaviors
Dynamic Preference Inference
Figure 2: The framework of SPOT-Trip. CE denotes the cross-entropy loss.
3.1 Knowledge-Enhame Static Preference Learning
Traditional out-of-town recommendation methods have attempted to model static preference with transition relationships [46] and geographic relationships [47]. However, they remain hampered by data sparsity and fail to align user preferences across different regions. Knowledge graphs (KGs) [ 555 enrich POI embeddings using additional semantic information, thereby strengthening user preference representations. Yet, directly integrating semantic and spatial information may lead to conflicting interactions [32]. Accordingly, we leverage KG approach solely to model the static user preferences from semantic perspective. Spatial information will be incorporated in the dynamic learning described in Sec. 3.2
Semantic Knowledge Aggregation. In the POI attribute knowledge graph, various relationships (e.g.. POI's category and corresponding region) encode distinct semantic information. Inspired by the graph attention mechanisms in [50] [2), we first introduce relation-awan knowledge embedding layer to reflect the heterogeneity of relations and -latcfn--pecific representations through parameterized attention mechanism. Towards that, we construct our relation-aware message aggregation mechanism between the POI and its connected entities in Gk, for generating knowledge-aware POI embeddings via heterogeneous attentive aggregator illustrated as follows:
knowledge graph connections and then gain entity-
exp(o(rT Wie | vi)) V=V+ Σ "),, EeEN, exp( Wie v eeA.
(1)
where NG, is the neighboring entities of POI various relations Te in GK. Rd and g represent the embedding POI and entity, respectively. The estimated entity- and relation-dependent attentive relevance during the knowledge aggregation process is denoted encodes the distinct semantics of relationships between POI v and entity e. means the concatenation of two embeddings. W E ddx2d denotes the weight matrix that is tailored the input POI and entity representations, is the activation function LeakyReLU for non-linear transformation. Additionally,
a(c, Te,u, v), which

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to further improve the multi- nulti-rellatonal semantic representation space for -tem dependencies, an alternative training is employed between the relation-awan aware knowledge aggregator and TransE (More details about the alternative training and its loss can be found in the Appendix A.1
Static Preference Alignment. Inspired by prior out-of-town recommendation approaches that derive user preferences through behavioral aggregation, we utilize an aggregator 4GGG(+) (i.e., average pooling) to consolidate the enriched POI embeddings, producing enhanced user hometown and out-of-town static preference representations: a = AGGs((Vi, ([=mm==)) û = ( (Iv:)==)) Subsequently, we infer user out-of-tows town preference by MLP based on their hometown preferences:
po (Wuu* + bs),
where Wg E Rddd and by E Rdd are trainable parameters. & denotes the activation function SILU. An Euclidean distance loss Ls is adopted to bridge the inferred preference and the actual out-of-town preference for static learning as follows:
Ls
P -
NEU
3.2 ODE-Based Dynamic Preference Learning
incorporate position embeddings and periodic hour encodings model the dynamic nature of user preferences. However, due to the irregular sampling 37 of check-in data, these approaches fail to capture the dynamic evolution of user preferences over actual time. Given the success neural ODE [8] in other research fields [34 30), we develop a neural ODE to model the continuous dynamic drift of user out-of-town preferences in latent space. To quantify the behavior event probability at each moment, we also formulate the preference inference point process (33,, where the instantaneous probability is described by the intensity function X(-) of a non-homogeneous Poisson process (NHPP) on the latent state. Overall, the Dynamic Preference Inference process can be defined as:
Previous trip recommenders 23 39
temporal
Pr ~ p(P..) dp. = (pi.), dt
(4)
in ~ (HPP(X(P:)), 1....,, N, ~ ~VV pp..) 1,.,, N,
(5)
(6)
where Pi represents the latent preference state at time t. f(-) and A(-) are MLPs for parameterizing ODE dynamics and the intensity function. For brevity, we denote pł, by P% over the remainder of this paper, and the corresponding joint distribution is:
N p(P (ln volN =P(P))(((m)) Pi) Il
(.. = NN0,1) I),
m=1
M Pi) = II X x(p)))xp(- (d
)d() ( N of.
where N is the normal distribution, O is zero vector, / is the identity matrix and age denotes hyper- parameter that sets the model's tolerance for discrepancies between observations and predictions. In the following, the detailed descriptions and inferences of each component are provided.
Latent Dynamics of Preference Drift (Eq. 4. Before modeling the latent ODE dynamies, the user's dynamic spatiotemporal behaviors including timestamps and spatial coordinates are first mapped into the latent space via linear transformations and new embedding layer E(-): vo. W,th.o + W, E(ph,,), where W, RRd and W, IR2xd trainable parameters. Note that the POIs' spatial features are treated solely as quantitative values because geographic information unavailable during the recommendation. To better infer the posterior of the latent initial state of preference D and model parameters, the variational inference [1] is used for latent modeling. We define approximate posterior qpf: Φ) with variational parameters posterior p(p (to Vol (o) with the objective of minimizing the Kullback-Leee -eibler divergence
approximate the true
(8)
5

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over the variational parameters to derive an estimate of the approximation of the posterior and model parameters. To avoid optimizing the local variational parameters 3 for each behavior trajectory in the dataset, we use amortization [ and define và as a reparameterization sampling from the output of a stacked Transformer encoder layer 40 Transp- To be specific, we first introduce an aggregation token, AGG, with learnable representation to indicate Transp to aggregate user hometown behaviors. This gives us the encoder input sequence: {{vm 1M Jm=11 AGG], and then we acquire the output aggregation token AGG at the last layer. Finally, we obtain i from AGG with the reparameterization sampling dependent specification of the sampling).
approximate posterior (see Appendix A.2 for
Temporal Point Process (Eq.5). After receiving the inferred preference Pa from the ODE solver in Eq. we employ the parameterized intensity function X(-) to map it to the intensity of NHPP. In order to ensure that the intensity function value is nonnegative and enhance its numerical stability, we further exponentiate the output of ((p))) and then add small constant.
Behavior Distribution Reconstruction (Eq.(6). In practice, rather than minimizing the KL diver- gence in [qq88diiretlyy we instead maximize the corresponding evidence lower bound (ELBO) for the dynamic behavior distribution reconstruction, and the total dynamic learning loss is defined as:
Nu
In A(P:) - /x(P)att
fD=
=
E ( In pVV. PiI
(D( n=1
WEl
(6) Expected restructured behavior log-lik
(fi) Expected temporal point process log-lik
[L((( (p(()) )
(114) KL berween prior and posterior
where the term (iii) can be computed analytically, computation of terms (i) and (ii) involves ap- proximations: term (i) is estimated via Monte Carlo integration using samples from the variational posterior, whereas term (ii) involves solving ODE with numerical solver and applying the trapezoidal rule to approximate the expected intensity integral. The ELBO maximized w.r.t. the model parameters and its detailed derivation is contained in Appendix A.3
3.3 Static-Dynamic Preference Fusion
To generate the logit Zu,n,; for user u at position n over each POI E ao we first construct a unified representation by combining the user's knowledge-enhaneed query representation Qo gddxd, the static preference po inferred in Sec. 3.1 and the dynamic preference sequence po {px y { inferred in Sec. 3.2 This joint representation is then fed into nonlinear prediction head to compute the logits for all POIs at each position n in the out-of-town region a.
Su,n,i Φ W n Q"I Po P
+ bR),
(10)
where W n E Addx bR E Rd are trainable parameters and a is the activation function LeakyReLU. Appendix A.3 contains more details about the acquisition of query representation Q". Consistent with previous works 39), the main recommendation loss is formulated as a cross-entropy loss:
u
exp Zu vg..
CR Enew No
log
(11)
Eiea, exp( Fum.i)
3.4 Optimization and Recommendation
Optimization. With Eqs. 3
9 and 11 we can jointly optimize the composite training optimization loss function in an end-to-end fashion as below:
L - L,Ls + B2DD + BgCR.
(12)
where 31. 32 and B3 are the hyper-parameters to balance the effects of the three losses in SPOT-Trip Trip.
Recommendation. When carrying out the out-of-town trip recommendation, our goal is to recom- mend a sequence of out-of-town POIs to a new user u" traveling to region a with a clear query. In particular, given u hometown check-in records and query Q {eg vg, N). we first derive her/his

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Table 1: The overall comparison between SPOT-Trip and baselines, where the best performance is marked in bold while the second-bes results are underlined. * denotes improvements that are statistically significant, where we use two-sided t-test with p- p-value < 0.05 31
Foursquare
Yelp
Method
F1(1)
PoirsPi(t) Full-Fi(1) Pull-Pairs/it
Fi:t)
PairsFi(1) Full-Fi(t) F Full- ((t)
PersTourr 26
0.0258
0.0016
0.1572
0.2074
Popularity
6
0.0261
0.0013
0.4423
0.1565
0.0257
0.0056
0.5065
0.2058
POIRank
0
0.0253
0.0019
0.4416
0,1582
0.0264
0.0079
0,5068
0.2093
GraphTrip
14
0.0295
0.0048
0.4498
0,1620
0.0289
0.0126
0,5107
0.2184
MarTrip 54
0.0311
0.0037
0,4530
0,1656
0.0301
0,0119
0,5117
0.2191
AR-Trip 39
0.0304
0.0045
0,4512
0,1673
0.0307
0.0153
0,5115
0.2204
Buse
0.0339
0.0069
0,4571
0,1698
0.0315
0.0149
0,5097
0.2215
Base KDDD 32
0.0375
0.0079
0.4606
0.1822
0.0341
0.0156
0.2256
Base
CNN-ODE 21
0.0367
0.0094
0.4578
0.1843
0.0326
0.0168
0,5124
0.2237
PPROC
0.0330
0.0071
0.1687
0.0334
0.0159
0,5110
0.2218
-rip
0.0400
0.0109
0.4723'
0.1960"
0.0399
0.0190
0.5261
0.2347'
Improvement
+6.67%
+15.96%
+2.54%
+6.34%
+17.01%
+13.90%
+2.63%
+4.03%
inferred static preference P and dynamic preference P following Eqs. 2 and Then we utilize Eq. [o generate each POI logit Zur .n, at the trip position n in region a and employ a stochastic sampling method (i.e., Top-p (2)) to recommend the intermediate POI(s) for trip completion. Note that actual timestamps are employed in Sec. Sec.3.22 during training to fully capture the temporal dy- namics; however, such precise time information not available during the recommendation phase. Consequently, we adopt the normalization of each query position surrogate time grid, thereby approximating temporal progression a computationally efficient manner.
Experiment
4.1 Experimental Setups
Dateset. The experiments are carried out on two widely-used travel behavior datasets: Foursquare and Yelp. We follow existing studies [47 32 to identify users with check-in activities in their hometown and other regions. The cneck-ins are reorganized to form the corresponding out-of-town travel records (u, ch c, Uh an). To improve the rationality, we filter three check-ins in out-of-town regions, or whose travel durations are shorter than 1 hour or longer than 30 days. The knowledge graphs are extracted from the supplementary descriptive information in two datasets. More details regarding datasets can be seen in Appendix B.1
who have fewer than
Baseline. We compare SPOT-Trip with 10 baselines, including established trip recommenda- tion methods without hometown information: PersTour [26). Popularity (6). POIRank (6], Graph- Trip [1]. MatTrip [54). AR-Trip [9], and Base; and 3 methods with hometown information: Base
+ KDDC (32], Base + CNN-ODE [21). and Base + PPROC (21). where Base denotes SPOT-Trip without KSPL and ODPL. More baseline details are provided in Appendix B.2 Evaluation Metrics. Following existing studies 14 23 39,, Fi and PairsFi, are adopted as the evaluation metrics, where higher values indicate better performance. As the out-of-town trip recommendation focuses on intermediate POIs, we calculate Fi and PairsF, without the origin and destination. In addition, enable more comprehensive comparison, we also report the exper- iment results with origin and destination, denoted as Full-F, and Puil-PiirsFi Please see the corresponding formulas in Appendix B.3
Implementation Details. We implement our model using the Pytorch framework on NVIDIA GeForce RTX 4090 GPU. We perform statistical testing, i.e., three times, with different parameters to enable fairer comparison, where averaged results are reported in our study. The learning rate set to 0.001 with Adam optimizer. Additionally, the batch size and training epochs are set to 32 and 1000, respectively. We provide more details about implementation in Appendix [.4]] Further, the parameters of the baseline methods are set based their original papers and associated code.
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4.2 Overall Performance
We compare SrOT-Trip with 9 baselines, where Table reports the F1, PairsFi, Full-Fi, and values. Generally. SPOT-Trip achieves the best results on both datasets across all evaluation metries, demonstrating its effectiveness. SPOT- Trip performs better than the best among the baselines by up to 17.01 % and 15.96% in terms of Fi and Pairs F1, respectively. We also observe that the performance improvements obtained by SPOT-Trip on the Yelp dataset exceed those on Foursquare in terms of intermediate POIs recommendation. This is because the sufficient semantic information of Yelp introduces more external knowledge for model training, enhancing model performance. Additionally, we observe that out-of-town trip recommendation- bbaed methods that use hometown information generally yield higher Fi and Pairsfi scores than those trip recommendaation-aasdd methods without hometown information, suggesting the significance of the static and dynamic user preference modeling with hometown context.
Further, compared to suboptimal baselines equipped with only single learning module, i.c., Base + KDDC, Base + CNN-ODE and Base + PPROC, SPOT:Trip achieves notable improvements. Specifically, on Foursquare, it improves the F1 and PairsFi by 6.67% and 15.96%, respectively, while the corresponding gains are 17.01% and 13.90% on Yelp. Moreover, under full-trip evaluations Full-Fi and Full-Pairs F1, which inherently include fixed origin and destination and therefore dilute relative gains, SPOT-Trip still consistently outperforms the baselines. These observations demonstrate the superiority of SPOT-Trip due to that it can learn static and dynamic user preferences comprehensively. It is worth noting that Base + PPROC exhibits inferior performance compared to Base CNN-ODE, potentially because of the complexity involved in predicting spatiotemporal event points, which largely depends on the design of the underlying predictive model.
4.3 Ablation Study
To gain insight into the contributions of the different components of SPOT-Trip, we evaluate three variants:
SPOT-Trip
wwo KS
Wele OD
wlo SI
4.00
(I) w/o KS SPOT-Trip without the KSPL module; (2)
1.75
w/o OD. SPOT-Trip without the ODPL module; (3) w/o
Score (x10
3,75
1.50
1.25
SI. SPOT-Trip without the spatial information in ODPL.
3.25
Score
1.00
Figure 3 shows the results on two datasets, SPOT-Trij Trip outperforms its counterparts with the KSPL module, ODPL module, and spatial information. This shows that these three components useful for effective out- of-town trip recommendations. In particular, the w/o KS variant shows 10.02% drop in F1 and 21.79% drop in PairsF, on Yelp, validating the effectiveness our framework in modeling users' static preferences. Furthermore, the w/o SI variant suffers significant performance degradation, especially in terms of Pairs/i on Foursquare and Fi on Yelp, demonstrating the necessity of incorporating spatial information into the static preference learning process. More results and discussions provided in Appendix C.I
3.00
8.75
Feursquare
Y2lp
Feursquare
Yelp
(4) Fg
(b) PairsFj
Figure 3: Performance of SPOT-Trip and its variants on two datasets.
1.12
1.25
4.05
1.25
4.00
4.000
1.00
3.90
1.04
3.90
1.00
1.00
Pairs 0410
X 3.75
0.96
0.75
3366
3,80
3.60
0.58
3,30
3..40
0.50
3.45
0.50
0.25
3.20
01.2
01.4
0..6
0..8
0.25
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.2
0.4
0.6
6.8
f2
Figure 4: The effects of discrepancy tolerance parameter (aTT)) ) and various loss function weights (8).
32 and (3) on the Foursquare dataset w.r.t. the Fi and PairsF, score.
4.4 Hyper-parameter Analysis
We study how sensitive the framework is to the tolerance parameter de= and loss function weights 31, 32 and 8g- The results are reported in Fig. 4 Specifically, dg- controls the standard deviation of the behavior noise. The results show that both Fi and PairsF first show increasing trend with
8

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Table 2: The effect of data sparsity. We sample different fractions of the training data.
Hometown during Training
Dataset
Method
40%
60%
80%
100%
Fi(1)
PairsFi(1)
Ft(1)
PairaPi(1)
Fi(t)
PasraFi(1)
Fi(t)
Pair=Fa1())
Base KDDC
0.0325
0.0038
0.0348
0.0058
0.0364
0.0069
0.0375
0.0079
Foursquare
Base CNN-ODE
0.0314
0.0046
0.0336
0,0052
0.0352
0.0074
0.0367
0.0094
SPOT-Trip
0.0347
0.0073
0.0369
0.0085
0.0401
0.0091
0.0400
0.0109
Basc KDDC
0.0321
0.0140
0.0331
0.0149
0.0338
0.0151
0.0341
0.0156
Yelp
Basc CINN-DD
0.0313
0.0327
0.0156
0.0333
0.0161
0.0326
0.0168
SrOT-Trip
0.0327
0.0162
0.0343
0.0178
0.0384
0.0216
0.0399
0.0190
the increase of dee and then decline gradually, where two exceptions are observed when dge is set to 0.3 and 0.4. This indicates a moderate dg- (e.g., 0.6 on Foursquare) would be good option for SPOT-Trip as it achieves a balance between latent dynamics and true behavior.
Besides, assess the effect of each loss weight (31, $2. 53) by varying one within the range 0.91 and setting the other two to 1-8 e.g., varying $1 with 32 B3 1-31 It can be observed that for the three parameters, the recommendation performance attains its peak when they are set to 0.3 0.4. This suggests that moderate loss weights help balance the contributions of different loss components during tuning. Based on this observation, we set B1 = 32 3g = in the final framework, assuming that each loss term is properly normalized ensure balanced influence. More results regarding parameter sensitivity can be found in Appendix C.2
4.5 The Effect of Data Sparsity
Tab. summarizes the performance of various methods trained with limited hometown data. We randomly sample 40%, 60%, and 80% of the hometown trajectories, while preserving the original sequence order and avoiding adjacent duplicate POIs to ensure data quality. It is observed that SPOT-Trip consistently outperforms the strongest baselines, highlighting its robustness in data- scarce scenarios. An interesting observation is that using only 80% of the hometown data leads better Fi on Foursquare and PairsFi on Yelp than using the complete data. We speculate that this improvement may result from the random sampling process, which potentially removes noisy information from the hometown check-ins.
Queryy Patis
Recomanended
Matriir
(a) AR-Trip
(b) -Tip
(c) SPOT-Trip (0)
(d) SPOT (D)
Figure 5: Visualizations of recommendation results for the user 2964 on Foursquare. (O) denotes a
query with single origin, while (D) denotes query with single destination.
4.6 Case Study
To intuitively show the effectiveness of SPOT-Trip. we provide a case study on Foursquare as shown in Fig.5] We choose AR-Trip for comparison due to its superior performance. The results show that the recommended intermediate POIs of SPOT-Trip exhibit a remarkable level of alignment with the ground truth. It is clear that SPOT-Trip can accurately trace the right out-of-town trip recommendation. Figs. (tcc 5 show that even with only the origin (SPOT-Trip (O)) or destination (SPOT-Trip (D)) provided, SPOT-Trip generates plausible recommendations (e.g., both the predicted destination and origin correspond to train stations), demonstrating its ability to capture realistic user travel patterns. We have provided more case studies in Appendix

---

5 Relate Work
Out-of-town Recommendation. Out-of-town recommendation aims to suggest the next likely new POI for users visiting unfamiliar regions. This task is particularly challenging due to issues such as cold start and interest drift. Early work, such as [13] addressed the problem by exploring social influence. Subsequent studies [53 43] primarily adopted latent Dirichlet allocation (LDA) to capture interest drift, incorporating user preferences and POI content. In addition, deep learning- based approaches have been proposed to tackle the problem more effectively. For instance, [46] leveraged neural topic modeling to conduct a fine- -ee--raaied analysis of users' travel intentions, while 48 exploited cross-city mobility matching mitigate data sparsity. [47] and [32 settled the more intractable pre-travel recommendation with the crowd behavior memory and causal relationship. Despite these efforts, limited attention has been paid to the out-of-town trip recommendation scenario.
Trip Recommendation. Earlier research on trip recommendation predominantly relied on planning- based approaches rooted in the orienteering problem 177. For example, Popularity [6] focuses solely on POI popularity and Markov (6] models transitions between POIs. Later methods began to move beyond pure optimization formulations. For instance, C-ILP [8] introduces context-aware POI embeddings via linear programming. However, these planning- blased methods struggle to capture the complexity and uncertainty of human mobility, motivating the rise of recent learning- based approaches. Transformer based models like Bert-Trip (23) treat trip recommendation as a sentence completion task to generate personalized itineraries that align with tourists' preferences and real- world constraints, while AR-Trip (39] leverages a prior position-based matrix to alleviate the issue of repetitive recommendations. While effective to some extent, these methods use only position embeddings to model preference changes and are limited in capturing irregular temporal dynamics, which our proposed SPOT-Trip is designed to overcome.
Knowledge Graph-enhanced Recommendation. Existing Knowledge Graph (KG))enhanced ap- proaches for general recommendation tasks can be broadly classified into three categories: embedding- based, path- path-based, and graph neural network (GNN)-based methods. Embedding- based methods, such [35. utilize mansition-based models (e.g.. TransR (27) to generate item representations via
entity embeddings. Path-based methods 16 45 enhance user-item connectivity by constructing meta-paths, yet they heavily rely on domain-speciff prior knowledge and manual path design. More recent efforts have shifted towards GNN based techniques [3 44), which leverage message passing across multi-hop neighbors capture complex relational structures. KGCL (51) and KGRec (50) further introduce joint self-supervised learning schemes to mitigate data noise but will increase computational overhead. Therefore, our framework alternately leverages embedding-bane and GNN-based methods to enhance the semantic representation of users' static preferences.
Ordinary Differential Equation. Neural ODEs [9] represent novel framework that extends discrete deep neural networks to continuous- time domains by modeling transformations as ordinary differential equations, effectively generalizing architectures such ResNet [19]. Owing to their strong performance and modeling flexibility, neural ODEs have found wide application across diverse areas, including traffic flow prediction (34,, time series forecasting [41), and continuous dynamical systems 15 [1] Recently, researchers have begun integrating neural ODEs with GNNs
by parameterizing the derivative of hidden node states for sequential recommendation [ 37]. In contrast to existing ODE approaches, we introduce neural ODEs to effectively capture the temporal dynamics of user preferences in out-of-town regions.
Conclusion
We present SPOT-Trip in this study, static-dynamic preference aware out-of-town trip recommen- dation framework, which features three modules. First, the knowledge-enhancee static preference learning module constructs POI attribute knowledge graph and employs relation-aware attention to generate enriched POI embeddings, capturing the static preferences. Second, the ODE-based dynamic preference learning module is proposed leverage neural ODEs with a temporal point process learning the continuous drift of dynamic user preferences. Together with the static-dynamic preference fusion module, SPOT-Trip delivers superior personalized trips for users traveling from their hometown to unfamiliar regions. Comprehensive experiments on two real datasets offer evidence that SPOT-Trip achieves state-of-the-ar accuracy. In the future, an interesting research direction is to apply SPOT- Trip to other spatio-temporal tasks, such as trajectory prediction.
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Acknowledgments and Disclosure of Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62476247, 62073295 and 62072409, "Pioneer" and "Leading Goose" R&D Program of Zhejiang under Grant 2025C01030, part by the Zhejiang Provincial Natural Science Foundation under Grant LR21F020003, in part by the Supcon Research Fund under Grant KYY-HX-20230888 and Lantai Research Fund under Grant XYY-HX-20240573, KYY-HX-20230366 KYY-HX-20250588.
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A Technical Appendix: Methodology Details
In this section, we provide more technical details of SPOT-Trip.
A.1 Alternative Training
As discussed Sec. 3.1 the relation- knowledge aggregator will alternately trained with Trans [4] to improve the multi-relational semantic representation space. 'hee core principle of this translation-base based knowledge graph embedding is to ensure that the sum of the head and relation embeddings (i.e., v and approximates the tail embedding e. To quantify similarity, we define fa(-) as an LI norm-based measurement function, i.e., fa = ||v + -el. Formally, the translation-based loss ErroneE is expressed
LrransE=
Σ
-Ino - fflvvv)))
(13)
(")
where the negative sample e' is generated by randomly substituting the tail in the observed triplet (v. r,e) from the knowledge graph Gx-
A.2 Approximate Posterior
We define the approximate posterior with = [W vhaa as
(Pf: vp) Mppi thus diag((ha))
(14)
where N is the normal distribution, and diag( (1o2) a diagonal matrix with vector t)aa on its diagonal. As discussed in Sec. 3.2 sb is obtained from AGG with the reparameterizztioon sampling, where we further break up va into pb thaa as follows:
S Linear (AGG), tha = exp [Linear [Liear(AGG))
(15)
where Linear(-) are separate linear mappings.
A.3
ELBO
Based on the definitions presented in the previous sections, the ELBO can be formulated as:
((P)) In p(pi, (tn:
EOIN
ELBO
dp
(Pp))
a(p)) In Ih P pi (n
dpi
((p))
/ ((p)) Inp(pi, (tn, vo1 v.
/ (pi)) Lup(tt)) (up(t)))ww1b9))pp lb))ddpppp
+ / ((()) p(pi) dpi
((p))
N
E
E In (v pi)]
In ((p)) / ((p
E Eg(())
(KL((p"))pp(?))
(16)
21

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Algorithm Optimization phase of SPOT-Trip
Input: POI knowledge graph Gk. user out-of-town trip records O, POI set V, training epochs E
Output: Out-of-town trip recommender Fe
1: Initialize POI/entity/relation embedding layers and other framework parameters,
2: for epoch
do 3: Update POL/entity/relation embedding layers with TransE (Appendix A.1 from Gx:
4: Get knowledge-award POI embeddings via Eq. and latent POI embeddings by E(-):
5: for each user with trip Ç = (u, c, Co. Oh: oo) do
6:
Get the static preference representations, i.e., a and u Obtain inferred out-of-town static preference P via Eq. 2
7:
8:
Calculate the static learning loss Ls via Eq.
3
9:
Obtain inferred out-of-town dynamic preference P = [P:]1 via Eq. Calculate the dynamic learning loss LD via Eq,
11:
Fusion preferences and compute the logits Zu,n.i for all POIs located in via Eq. 10
Calculate the main recommendation loss LR via Eq. IT]
13:
Sum Ls, LD and La to obtain L (Eq. 12). Then, update Fo by minimizing L; end for
14:
15:
end for
return Fo-
Algorithm 2 Recommendation phase of SPOT-Trip
Input: User u², hometown check-ins čh, query Qu = {og, ve, Nj, region o Output: Out-of-town trip T
1: Obtain the inferred static preference P and dynamic preference P via Eqs. and
2: Fuse preferences and compute the logits Zu,n, for all POIs located in a via Eq
3: Initialize recommended trip [e]];
4: for n = 2 to N do
5: Select eg ~ Top-P(2u,n,);;
6: Append 2n to T;
7: end for
8: Append vyy = ve to
9: return [ug. ( , v
A.4 Query Representation
user's query typically reflects her/his inherent semantic preference. Therefore, given the query Qo = {rg, ,x NJ. we use semantic knowledge aggregation explained in Sec. 3.1 to obtain the knowledge-enhancee query embeddings Q Then we combine Q" with positional embeddings N [n) [ to initialize a Transformer encoder Transq, thereby gaining the position-aware query representation Q Transo(Q" [[]]
A.5 Optimization and Recommendation Phases
The detailed algorithm for the optimization and recommendation phases of SPOT-Trip are summarized in Algorithm D and [ facilitate understanding and implementation. At the beginning of the optimization (Algorithm() II. the POI, entity, and relation embedding layers and other parameters in the framework are initialized (line 1). Next, knowledge-aware POI embeddings are obtained via alternating training, while latent POI embeddings are learned through separate embedding layer (lines 3-4). Next, calculate the static learning loss (lines 6-8), the dynamic learning loss (lines 9-10), and the main recommendation loss (lines 11-12). Through the sum of these three losses (line 13), we can optimize
whole SrOT-Trip. For the recommendation phase (Algorithm 2 we first obtain the inferred static preference and dynamic preference for a new user (line 1). Based on these preferences, logits for all POIs within the target region are computed for Top-P sampling (line 2). Finally, the recommended trip is generated according to the user's 's query (lines 3-9).
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Table 3: Statistics of the two datasets.
Raw Records
Knowlodge Graph
Datasel
#Hometown Check-ins
#Out-of-town Check-ins
#Users
#Regions
#POIs
#Check-ins
#Relations
#Entities
#Triples
Foursquare Yelp
3.007
21
23.884
126,219 78,882
109,225 58,403
16,994 20,479
411 53,549
47,768
4,417
214
29,930
8
353.918
B Additional Experimental Details
We proceed to provide more details about the experimental settings.
B.1 Dataset
The experiments in this study are conducted on two widely used check-in datasets: Foursquare and Yelr Table presents the statistical summary of these datasets, including details on check-in records and knowledge graph characteristics. For check-in data processing,
first identified users who had check-ins ins in both their hometown and out-of-town regions. Each user's check-in sequence was then reformulated into out-of-towe travel record & (u, Ck Co ah. ao) (see Definition 3). To ensure dataset quality, we filtered out Points of Interest (POIs) visited fewer than two times and removed users whose travel records did not meet the following criteria: (1) c 2 (2) č 3; and (3) the frequency of (az, ao) 2 10. Furthermore, we normalized the timestamps and geographic coordinates (latitude and longitude) of all check-ins to the [0, range to facilitate model training. Subsequently, the datasets were randomly partitioned by user into training, validation, and testing sets with a ratio of 80%, 10%, and 10%, respectively. To ensure fairness in evaluation, all users were anonymized. For the construction of knowledge graphs, we generated entity-specific relations using various types of auxiliary information, such as categories, star ratings, user reviews, and associated regions.
B.2 Baseline
There are groups of state-of-the-art baselines that compared within this paper, i.e., 7 trip recommendation-base baselines and 3 out-of-town trip recommendation based baselines.
Trip Recommendation- Baselines. These methods primarily focus modeling POIs within the target out-of-town regions, while paying limited attention to the users' historical check-in behaviors in their hometowns.
PersTour 26 It treats trip recommendation as an orienteering problem and generates trip by leveraging the features of POIs while respecting given time budget.
Popularity 66. recommends the most popular or frequently visited POIs to a user at each query candidate position.
POIRank 6 generates travel trajectory by first ranking POIs using the RankSVM 24 method, and then sequentially connecting them based on their ranking scores.
GraphTrip 14 This method proposes two-stage graph learning framework to model multiple heterogeneous POI graphs, and designs a dual-grained mobility module to capture both coarse- grained category information and fine-grained transitional patterns among POIs.
(LSTM) based encoders to learn users' category preferences and the geographical proximity of POIs, followed by an attention-based LSTM decoder that generates user-preferred trips with the aid of an optimized search strategy.
MatTrip [54] This work employs dual
AR-Trip 39
This approach is based on Transformer encoder-only architecture and incorporates additional prior position information to recommend trips with low repetition rates.
Base. It is simplified version of our framework that removes all hometown-relater information learning and serves as baseline to examine the contribution of hometown -ware modeling.
hhups::/sstes. hwtp::/www.......oooooo/////o//. http:://wwww.yelp..oott///daeede
23

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Out-of-town Trip Recommendation- based Baselines. These baselines distinctively incorporate supplementary historical hometown information for out-of-town recommendations.
KDDC introduces a knowledge graph approach that strengthens semantic interaction and leverages disentangled causal metric learning to align recommended POIs more closely with the target preferences. In the Base + KDDC variant, the knowledge graph approach is integrated strengthen the Base's static semantic preference alignment ability.
Base + KDDC
We isolate the module ODPL from SPOT-Trip and replace the Transformer encoder before the Neural ODE with CNN encoder support dynamic learning in the Base framework.
Base + CNN-ODE [21].
Base + PPROC 211. PPROC combines neural spatiotemporal point processes (38 and neural partial differential equations 7 to improve the prediction of observations probabilistic locations and timings. serves as the dynamic preference learning module the Base PPROC variant.
B.3 Evaluation Metrics
As described Sec. 4.1 our evaluation employs four metries: Fi, PairsFi, Full-Fi, and Full- PairsFi. Formally, let [vf, 22 ....... " be the generated trips by method and r* be the ground truth, the calculation procedures of these metrics are detailed as follows.
12:N-1] O Tp.N-1) !!
Recall TaaN-1) A 72:N-11
Precision
T2:N-11
2 Precision Recall
(17)
Precision + Recall
Ne
No
PrecisioN pairs
Recall pairs
12:N-11 111
(T2.N11)
2
No > 0, x Precisionpaire X Recall
Pairsh F
Precision Recall Recallpaire pairs
(18)
0,
N₂ N==00 O,
where No is the number of correctly ordered POI-pairs in the recommendation. (T)NN-1) are the total number of ordered pairs, respectively. Correspondingly,
( 1) and
|T |T|
Full-Recall | n
Full-Precision
|T"
Full-Fi 2 x F Full-Precision x Full-Recoll fall-Precision HalllRecall
(19)
N
N
Full- Soll-Precision.poam pairs
Full-Recally pairs
[
|7'|
2
2
x Full- Prllisseciion pairs X Full- Mell-Recallpasss pairs
N > 0.
Full-PairsFi
Pull-Precisson + Recoll pairs
(20)
N 0.
Due to the less precise evaluative nature of Full-F, and Full-Pairs we report them only in the overall comparison experiments.
24

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SPOT-Trip w/o KS
w/o KA
w/o PA
w/o ST
w/o (OD+KA)
w/o AT
w/o OD
w/o (OD+PA)
4.00
4.00
1.05
1.80
Score (x]0-)
3.75
3.75.
0.90
Score (x10
1.60
3.50
3.50
0.75
1.40
3.25
Score
3.25
0.60
1.20
3.00
3.00
0.45
1.00
Foursquare
Yelp
Foursquare
Yelp
(44 F1
(b) PairsFi
Figure 6: Performance of SPOT-Trip and its variants on two datasets.
B.4 Implementation Details
We re-implement the baselines and their hyper-paramen parameters based on the details provided in their original papers and publicly available source codes TiAM For the two baselines that require periodic information. Graph-Trip [4] and AR-Trip [3),, we provide additional hour-of-day features input. In contrast, PPROC [2D relies on both temporal and spatial points to infer preferences at specific spatiotemporal locations. To support this, we additionally design two separate gated recurrent unit (GRU) layers to predict the potential time and location points for future trips. Following [39], we fix the hidden size of all embeddings to 32 in all our experiments. The optimizer uniformly chosen as Adam with an initial learning rate of 0.001 and L2 regularization with weight of 10 To avoid overfitting, we adopt the early stop strategy with an 8-epoch patience. In addition, the parameters of our framework and all its variants are consistently set as follows: Consistent with [21], the number of Transformer layers in module ODPL set to 4, while both f(-) and X(-) are implemented as 3-layer MLPs. The latent dimensions of these MLPs are searched from {16, 32, 64, 128, 256), with 128 selected as the optimal value based on validation performance. Similarly, we use a differentiable dopri5 ODE solver with rlol = alol 10-5 from torchdi [ package [9] For the static-dynamic preference fusion (Sec. 3.3). the number of Transformer layers is set to 1, following the configuration in [39]. All Transformer layers employ 4 attention heads. The hyper-papree parameter ago is tuned separately for each dataset, with the optimal value set to 0,6 for Foursquare and 0.4 for Yelp. In the optimization stage, the weights of the loss terms B1, B3 and B3 for two datasets are 1.
C Additional Experimental Results
In this section, we will show more experimental results to further analyze the effectiveness of our SPOT-Trip.
C.1 Ablation Study
(w/o KS, w/o OD, and w/o SI), Fig. 6 presents more comprehensive ablation study involving the removal of various modules and submodules. These include: (1) w/o KA. SPOT-Trip removes the semantic knowledge aggregation in KSPI,; (2) w/o AT. SPOT-Trip removes the alternative training in KA; (3) w/o PA. SPOT-Trip removes static preference alignment in KSPL; and two combination removals: (4) w/o (OD+PA). SPOT-Trip makes query embedding; and (5) w/o (OD+KA).
Beyond the three variants introduced in Sec. 4.3
recommendations solely based on the knowledge-enhant SPOT-Trip utilizes the raw hometown representation, without semantic enhancement, to support the recommendation. Overall, the performance degradation observed in each variant demonstrates the effectiveness of every component within the SPOT-Trie architecture. In terms of F; score, the most pronounced performance drop was observed for the w/o AT variant on Foursquare and the w/o (OD+KA) variant on Yelp. This discrepancy may stem from dataset scale: Foursquare's 's sparse triples require additional training, whereas Yelp's larger dataset risks noise without sufficient semantic augmentation. Regarding the PairsFl score, the w/o (OD+PA) ablation on Foursquare and the w/o
hhttss//jitubb.oo//aaannnnggaaan
httt:://gthhbbcom///ssmtttt//
25

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Table 4: Several Component Replacement Ablation Experiments.
Foursquare
Yelp
Method
Fi
PairsFi
Fi
Pairs
SPOT-Trip
0.0400
0.0109
0.0399
0.0190
replacements of two main modules
Base KDDC 332 + ODPL
0.0385
0.0089
0.0361
0,0174
Base + KSPL + CNN-ODE [21]
0.0390
0.0107
0.0351
0.0186
Base + KSPL Transformer
0.0362
0,0063
0.0370
0.0172
Base KSPI. + GRU
0.0341
0.0070
0.0358
0.0169
The replacements of the alternative training method
TransR 27
0.0378
0.0091
0,0366
0.0182
SEEK (2)
49
0.0348
0.0082
0.0342
0.0146
SEEK (4)
0.0376
0.0087
0.0346
0.0154
SEEK (8)
0.0394
0.0108
0.0361
0,0183
SEEK (16)
49
0.0346
0.0073
0.0335
0.0134
The replacement the parallel design
SPOT-Trip (ODPL- KSPL)
0.0351
0.0078
0.0344
0.0153
Table Activation Function Replacement Ablation Experiments. Triple ((1)-P(2)-09))) denotes the activations in Eqs I and T0 L/S/G is LeusReLU/SiLU/GEUU
( ((10)
Foursquare-Ir Fi
Foursquare-Pairste
Yelp-Fi
Yelp-PairsFi
L-L-
0.0386
0.0094
0.0378
0.0175
L-L-S
0.0391
0.0101
0.0384
0.0181
L-G
0.0407
0.0097
0.0391
0.0178
L-LLL
0.0400
0.0109
0.0399
0.0190
LSS
0.0397
0.0105
0.0394
0.0182
LSG
0.0391
0.0103
0.0389
0.0179
LGL
0.0381
0.0085
0.0387
0.0193
LGS
0.0387
0.0100
0,0391
0.0181
LGG
0.0381
0.0097
0,0392
0.0185
0.0378
0.0095
0.0381
0.0187
SLS
0.0389
0.0101
0,0394
0.0185
SLG
0.0389
0.0104
0.0398
0.0181
SSL
0.0402
0.0107
0.0380
0.0184
sss
0.0401
0.0103
0.0389
0.0181
SSG
0.0392
0.0108
0.0379
0.0178
S-G-L
0.0384
0.0102
0.0385
0.0184
SGS
0.0397
0.0103
0.0381
0.0175
SGG
0.0391
0.0103
0.0394
0.0191
GLL
0.0395
0.0108
0.0391
0.0179
GLS
0.0381
0.0103
0.0394
0.0187
GLG
0.0394
0.0101
0,0397
0.0195
GSL
0.0384
0.0107
0.0378
0.0178
GSS
0.0389
0.0102
0.0394
0.0184
G-S-G
0.0394
0.0095
0.0388
0.0190
GGL
0.0381
0.0084
0.0398
0.0185
G-G-S
0.0375
0.0098
0.0387
0.0183
G-G-G
0.0391
0.0105
0.0391
0.0188
26

---

4.00
130
4.00
1.80
3,80
1.95
1.75
3.50
1.4
A77
1,55
1.80
Paim Ox
3,50
1.25
3.25
1.50
135
3.25
1.35
1.50
Pain
3.40
1.20
1.20
3.00
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.2
0..4
0.6
0.8
1h
Figure 7: The effects of discrepancy tolerance parameter (0)=) and various loss function weights (81.
32 and 33) on the Yelp dataset w.r.t. the F1 and P score.
4.00
Pair-Fi (XCID <
1.800
33.00
Pairsl1 (xci
1.00
3.60
1.60
03.00
0.90
3.30
1.40
3.70
0.80
3.00
3.60
0.70
(a) Foursquare
(b) Yelp
Figure The effects of the number of hidden states of MLPs in both f() and X(-) on two datasets.
SM ablation on Yelp exhibit the largest performance drops. The findings demonstrate the crucial importance of learning static and dynamic preferences.
In addition the removal-baas bbssdd ablation studies, further conducted series of component replacement ablation experiments in Tab. 4 Firstly, we replace the two main modules our framework with top-performing baselines: KDDC (Base KDDC + ODPL) and CNN-ODE (Base + KSPL + CNN-ODE), to further assess the relative contribution of each component. Compared to our full framework, the variant Base + KDDC ODPL results in 22.47% drop in PairsPi on the Foursquare dataset, while Base + KSPL + CNN-ODE leads to 13.68% decrease in Fi on Yelp. To further examine the advantage employing ODE-based temporal modeling. we also replace the ODE module with alternative sequence modelers. including Transformer and GRU, forming Base + KSPL + Transformer and Base + KSPL + GRU. Both variants exhibit noticeable performance degradation across datasets, suggesting that while Transformer and GRU can capture temporal dependencies to some extent, they struggle represent the continuous and irregular temporal dynamics effectively modeled by ODEs. These results highlight the unique advantages and effectiveness of our framework design. Secondly, assess the effectiveness of our alternating training strategy, we replace TransE in KSPL with Transk 27 and SEEK (49. TransR models entities and relations in separate vector spaces to capture complex relational patterns, while SEEK introduces segmented embeddings and interaction-aware scoring functions to enhance expressiveness. For SEEK (k), we set the number of embedding segments & to (2, 4, 8, 16). The observed performance drops indicate that TransE is better suited for our framework, likely due to its low model complexity and stable optimization behavior under alternating training. contrast, more expressive models such as TransR and SEEK may require more careful tuning to fully realize their potential. Next, we replace the original parallel architecture with a sequential variant, where the knowledge-enhanced POI embeddings are directly used for modeling users' dynamic preferences, denoted SPOT-Trip (ODPL KSPL). This cascaded design significantly underperforms our parallel architecture, likely due to interference between static and dynamic preference signals when modeled in non-decoupled manner. This result further validates the advantage of our parallel modeling design. Finally, we additionally performed an exhaustive activation-functioo replacement ablation covering all 3 27 combinations of d()), (12), ((10) E [LeakyReLU, SiLU, GELU). The full results are reported in Tab. 5 Across the 27 configurations. although L-L-G achieves the highest F1 on Foursquare (0.0407) and G-L-G attains the highest PairsFi on Yelp (0.0195), our original activation setup consistently performs best overall across both datasets and other metrics. Moreover, the performance of alternative configurations varies only slightly, reflecting the model's s strong stability with respect to the choice of activation functions.
C.2 Hyper-parannn Analysis
Fig-1 7 illustrates how varying the tolerance parameter ag- and the loss function weights 3,, Ag and 8g. affects the performance of SPOT-Trip on the Yelp dataset. This line chart result of ag- shows similar
27

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Cirios
Debda
Dolbaike
Orida
Cinnaddutrat
Recommncndde
Doliaake
Upensterettagg
(a) AR-Trip
(b) Trip
(c) SPOT (O)
(d) SPOT-Trip (D)
Recommendation results for user 1090 on Foursquare
P Dipeetreett
3 Upensienntiaag
(c) AR-Trip
(f) SPOT-Trip
SPOT (O)
(h) SPOT-Trip (D)
Recommendation results for user 1544 on Yelp
Figure 9: Visualizations of recommendation results for users on Foursquare and Yelp.
trend to the result in Fig. as dze increases, both F1 and PairsFi scores rise to a maximum at 0.4 and then gradually fall, with only minor rebound at 0.7. Therefore, setting de- 0.4 balances model expressiveness with fidelity to real user behavior. For 31, 82 and $3, we observe that F and PairsFi generally peak when / 0.3 or 0.4. The only notable exceptions occur at 0.3 and 33 = 0.6, where performance slightly deviates. Nevertheless, these results confirm that moderate weighting yields the best balance across loss terms. Accordingly, we retain 31 B2 33 in our final framework, assuming proper normalization of each component.
Moreover, simultaneously vary the latent dimensions of the ODE functions f(-) and X(-) from 16 to 256 to study their joint impact on user dynamic preference modeling in SPOT-Trip. As shown in Fig. 8 vary the latent dimensions from 16 to 256 and evaluate the performance both Foursquare and Yelp datasets using F1 and Pairs F1 as metrics. On Foursquare, both F1 and PairsFi initially decrease at smaller dimensions (32 and 64), but sharply increase 128, which yields the best performance. A slight degradation is observed at 256, likely due to overfitting or increased optimization difficulty. A similar trend is observed Yelp, where performance significantly improves with larger dimensions and peaks at 128 before plateauing or dropping slightly at 256. These findings suggest that the choice of latent dimension also plays a critical role Neural ODE- based preference modeling. Extremely small dimensions may limit expressiveness, while overly large dimensions can introduce instability. Thus, latent dimension of 128 is adopted in our framework.
C.3
Case Study
We present more case studies for user 1090 on the Foursquare dataset and user 1544 on the Yelp dataset in Fig. 9 To avoid visual overlap and improve readability, some marks and lines have been reduced in size or omitted. The results display that as the number of intermediate query points increases (i.e., the user 1090's), AR- recommendatioon become completely off-target, whereas SPOT-Trip tracks the ground truth closely, demonstrating our framework's 's robustness to varying user query lengths. Notably, SPOT-Trip's performance drops when handling queries that specify only an origin or only destination. In future work, we will strive enhance the method's capability to meet such specific user requirements.
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D Limitation
Despite the superior overall performance of SPÖT-Trip. Fig. (b) and (g) exhibit repeated recommen- dations at proximate positions, which may undermine trip diversity. While AR-Trip 39 incorporates a prior position matrix partially mitigate this repetition, is prone to error propagation: an incorrect POI recommendation at position can mislead predictions at other positions, resulting in compounded inaccuracies. This cascading effect may ultimately degrade the quality the recommended trip. In future work, we plan to explore more robust and context-aware multimodal frameworks [36 [2 28 that balance recommendation accuracy and diversity, paving the way toward more adaptive and user-centric recommendation systems.
29


Reviews:
[{"review_id": "opZvbLtsPH", "reviewer": "Reviewer_8nVL", "summary": "This paper addresses the out-of-town trip recommendation problem, which is of practical importance since users typically lack travel histories in the cities they plan to visit. The authors propose a method that first learns user representations from the source city, then transfers them to infer representations in the target  city for recommendation. Temporal dynamics are encoded using Neural ODEs. The proposed model achieves SOTA  results and is supported by extensive experiments and ablation studies. The paper is well-written, and the visualizations are of high quality. However, I have major concerns regarding novelty and over-claiming, particularly in comparison with a recent work KDDC [1], which appears to tackle a highly similar problem with overlapping methodology and visualizations. In its current form, this paper reads as an incremental extension of KDDC, and the claim of being the “first systematic study” seems inaccurate.\n[1] Liu, Yinghui, et al. \"Kddc: Knowledge-driven disentangled causal metric learning for pre-travel out-of-town recommendation.\" *Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, Jeju Island, Republic of Korea*. 2024.", "questions": "1. Please explicitly clarify the differences between your work and KDDC [1] in terms of problem formulation, methodology, and contributions.\n\n2. The claim that this is “the first systematic study” on out-of-town recommendation is questionable. Please justify this statement carefully, or consider revising it to avoid misleading claims.\n\n3. Neural ODEs can be computationally expensive to train. Have you explored alternative temporal encoding mechanisms (e.g., RNNs, transformers)? The current ablation study only removes the component but does not compare alternative encoders. This would help justify the use of Neural ODEs beyond novelty.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 1, "strengths_and_weaknesses": "Strengths:\n\n1. The paper targets a practically important and realistic problem—users usually lack trajectory data in out-of-town scenarios.\n\n2. The proposed framework is well-designed and technically sound, with each component thoughtfully integrated.\n\n3. The model achieves SOTA performance and is evaluated through extensive experiments and ablations.\n\n4. The paper is well-written, and its figures are aesthetically pleasing and professionally rendered.\n\n5. The released codebase is of high quality and supports reproducibility of the main results.\n\nWeaknesses:\n\n1. The paper addresses the same problem as KDDC [1], which was published in IJCAI 2024. Despite this, it claims to be “the first systematic study to learn out-of-town trip recommendation,” which could be viewed as an over-claim.\n\n2. Several figures and contents—including portions of the method diagram—bear a striking resemblance to KDDC, raising concerns about the level of novelty. As it stands, the paper feels like an incremental extension of KDDC.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the positive and encouraging evaluation of our work. We are especially grateful for your recognition of the practical value of the problem, the technical soundness of our framework, the thoroughness of our experiments, and the quality of both the writing and codebase. We also sincerely appreciate your constructive feedback and the time and effort you dedicated to reviewing our manuscript. Below, we carefully address the weaknesses and questions you raised.\n\n**Response to Weaknesses**: \n\nW1: While both our work and KDDC [1] address out-of-town scenarios, we would like to emphasize that our task formulation is fundamentally different from that of KDDC in terms of input conditions and recommendation granularity. Specifically, KDDC addresses a pre-travel decision-making problem, where the system recommends the next out-of-town POI from unknown regions to users who have not yet selected a travel destination. The goal is to suggest which POI to visit next, given the user's historical hometown records. In contrast, our work targets a post-destination trip recommendation task. We aim to recommend a sequence of POIs (i.e., a full trip trajectory) to users who have already chosen (or arrived at) a specific destination region. Our goal is to help these users plan meaningful intra-region journeys in response to user-issued queries that specify a starting point, an ending point, and a desired number of POIs to visit. As illustrated in Fig.1 of our paper, our setting reflects a significantly different application scenario that prior out-of-town recommendation works [2][3], including KDDC, do not cover, and the two problems serve users with distinct needs at different stages of their travel decision pipeline. We hope this clarification addresses the concern regarding the novelty of our problem definition.\n\nW2: We acknowledge that a small portion of our method illustration (the design and color scheme of the *semantic knowledge aggregation* module) bears some resemblance to KDDC. This is because such an aggregation module is a common and useful component in knowledge-driven recommender systems [4][5]. Beyond this overlap, our methodology in technical implementation differs from KDDC's. While KDDC employs a segmented pre-training scheme and a knowledge aggregation module to enhance POI representations before applying disentangled causal learning to recommend the next out-of-town POI, our approach takes a different route. We adopt a more lightweight strategy by using TransE-based alternate training to integrate knowledge, and we restrict this component solely to modeling users’ static preferences. \\\nMore importantly, our framework introduces a novel ODE-based module to capture dynamic user preference transitions in out-of-town regions—another critical capability for our sequential trip recommendation setting. Compared to KDDC’s task, which only requires identifying a single POI that matches the user's static out-of-town preference, our task involves modeling how user preferences evolve over time and location within a destination region, making dynamic modeling essential. To better illustrate the difference, a tabular is included in our response to Q1. \\\nMoreover, in Tables 1, 2, and 4 of our paper, we include several variants where we replace our knowledge-enhanced module with the one from KDDC. The consistent improvements across both datasets show that our approach is more effective.\n\n**Response to Questions**: \n\nQ1: To supplement our responses to W1 and W2 above, we summarize the distinctions below:\n\n|Aspect|KDDC [1]|SPOT-Trip (Ours)|\n|-|-|-|\n|**Problem Definition**|Pre-travel out-of-town recommendation: Recommend an out-of-town POI matching user interest|Out-of-town trip recommendation: Plan a full POI sequence|\n|**User Input**|Historical check-ins in hometown|Historical check-ins in hometown and user query (start/end points, number of POIs)|\n|**Recommendation Output**|Next POI|Intermediate POI(s)|\n|**Knowledge Graph Training**|Segmented pre-training|Alternative training using TransE|\n|**Preference Modeling**|Static only|Static + dynamic|\n|**Technical Innovations**|Serial modeling: semantic preference learning first, then disentangled causal inference|Parallel modeling: static semantic preference learning + ODE-based dynamic preference learning|\n\nTo further address your concern, we will incorporate explicit discussions of these differences in the Related Work and Baseline sections of the revised manuscript.\n\nQ2: In addition to the above validation from the out-of-town recommendation perspective that we are the first to study the out-of-town trip recommendation problem, we further justify our novelty from the trip recommendation perspective. Prior trip recommendation methods have not yet distinguished local users from those arriving from other regions, nor solved the preference drift that occurs when a user travels outside their hometown. Therefore, we introduce the out-of-town trip recommendation task and devise a dedicated method to address it.\n\nQ3: Thanks for the question. We choose Neural ODEs not for novelty, but because they offer continuous-time modeling capabilities that are critical in our task. Unlike RNNs and Transformers that operate over discrete and often fixed time steps, Neural ODEs, when integrated with point process tools, allow us to model travel trajectories with irregular time sampling from a probabilistic perspective, enabling a flexible characterization of continuous-time user out-of-town dynamic preferences. \\\nFollowing the reviewer’s suggestion, we have implemented two additional temporal modeling variants: Base+KSPL+GRU (RNN-based) and Base+KSPL+Transformer. In both variants, we retain the overall architecture of our model, replacing only the ODE-based dynamic preference learning component with GRU and Transformer decoders, respectively. Specifically, both variants autoregressively generate the user’s dynamic preferences over a fixed number of time steps. As shown in the following table, our ODE-based method (SPOT-Trip) consistently outperforms both the RNN variant (Base+KSPL+GRU) and the Transformer variant (Base+KSPL+Transformer) on both datasets across all evaluation metrics:\n\n|Method|Foursquare-$F_1$|Foursquare-$PairsF_1$|Yelp-$F_1$|Yelp-$PairsF_1$|\n|-|-|-|-|-|\n|**SPOT-Trip**|**0.0400**|**0.0109**|**0.0399**|**0.0190**|\n|Base+KSPL+Transformer| 0.0362|0.0063|0.0370|0.0172|\n|Base+KSPL+GRU|0.0341|0.0070|0.0358|0.0169|\n\nThis demonstrates the superiority of Neural ODEs in modeling continuous-time dynamics, which is particularly suitable for sparse and irregularly sampled POI trajectory in out-of-town scenarios. We will include these additional results in Table 4 of our revised manuscript to substantiate our design choices. Thank you once again for this valuable and constructive suggestion.\n\n**Reference:**\\\n[1] Liu, Yinghui, et al. \"KDDC: Knowledge-driven disentangled causal metric learning for pre-travel out-of-town recommendation.\" IJCAI. 2024.\\\n[2] Xin, Haoran, et al. \"Captor: A crowd-aware pre-travel recommender system for out-of-town users.\" SIGIR. 2022.\\\n[3] Xin, Haoran, et al. \"Out-of-town recommendation with travel intention modeling.\" AAAI. 2021.\\\n[4] Yang, Yuhao, et al. \"Knowledge graph self-supervised rationalization for recommendation.\" KDD. 2023.\\\n[5] Yang, Yuhao, et al. \"Knowledge graph contrastive learning for recommendation.\" SIGIR. 2022."}, {"review_id": "tHObOfGC4J", "reviewer": "Reviewer_q38N", "summary": "This paper addresses the problem of recommending a sequence of places to visit in a region where they might not have travelled before. This problem is challenging due to the sparsity of data about user preferences in the new region. Their preference learning scheme models both long-term (static) preferences and short-term (dynamic) preferences, where the dynamic preferences are influenced by factors such as the time and location of the user. A method for static preference learning is proposed, which uses a knowledge-graph approach that can aggregate and align user preferences from different regions, thus addressing the data scarcity challenge. A method for dynamic preference learning is also proposed, using a novel combination of neural ODE and temporal point process approaches. Finally, a fusion framework is proposed to combine the static and dynamic preferences in order to recommend a sequence of places to visit, given the origin and destination of the requested tour. Empirical analysis shows an improvement in F1 score for the recommended places and pairs of places, in comparison to several other learning based methods.", "questions": "Can you more carefully delineate the novelty of your work with respect to earlier methods that learn point of interest categories, which enable preferences to be learned from one region and applied to another region?\n\nCan you empirically compare your approach against such systems in order to demonstrate its advantages?\n\nYou mention in Limitations section that your method sometimes makes repeated recommendations at proximate locations. Some previous approaches like those listed above take into consideration the diversity and distance between recommended locations, and try to design tours that reflect a given budget for time and distance. While such budgets are potentially out of scope for your current paper, can you quantify and discuss the quality of the tours that could be constructed from the recommended locations, in terms of diversity, time and distance travelled?", "limitations": "Please see suggestions in questions.", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\nThe paper presents a novel approach to modelling long-term and short-term user preferences when recommending points-of-interest to a user.\n\nThe paper provides an interesting knowledge-graph and embedding alignment approach to address the problem making out-of-town recommendations, where data on point-of-interest preferences is scarce.\n\nWeaknesses:\n\nThe paper claims to be the first study of a new problem of out-of-town trip recommendation. While that might be the case for this particular style of deep learning recommenders, the paper does not seem to consider prior work on this problem that uses an optimisation approach, for example, Huang et al, “Multi-Task Travel Route Planning With a Flexible Deep Learning Framework” IEEE Transactions on Intelligent Transportation Systems 2020 or Lim et al, “Personalized Tour Recommendation based on User Interests and Points of Interest Visit Durations” IJCAI 2015, and others, which can make use of point of interest categories.\n\nWhile the paper provides an empirical comparison of performance against several learning based approaches, it does not provide a comparison against some of these optimisation-based approaches that learn categories of interest and hence can be applied to new cities.", "was_revised": true, "final_justification": "While the authors have made a new contribution through this work, based on the discussion I consider that compared to the body of prior work in this field, the current paper is an incremental advance.", "rebuttal": "Thank you for your detailed comments and feedback. We sincerely appreciate your time and effort in reviewing our manuscript. We will address the weaknesses and questions you raised below.\n\n**Response to Weaknesses and Questions**:\n\nW2&Q1: We agree that prior optimization-based approaches leveraging POI categories provide a way to generalize user preferences across regions. In fact, our method also makes use of POI category information, but in a more structured and expressive way. Specifically, as described in line 553 of our Appendix, we incorporate POI categories in the construction of a knowledge graph, where we generate entity-specific relations using multiple types of auxiliary information--including categories, star ratings, user reviews, and associated regions. This allows us to go beyond simple category matching and instead learn richer semantic representations of POIs through knowledge-enhanced embedding. \\\nMoreover, relying solely on POI categories, as done in previous work, is insufficient to capture the nuanced interest drift [1] that occurs when users travel across regions--i.e., the same user may exhibit different check-in behaviors in different cities. To address this, we introduce a knowledge-enhanced preference alignment module that explicitly learns and adapts to such cross-region preference shifts. We hope this clarifies the distinction between our work and earlier methods, and highlights the novelty of our approach in addressing the unique challenges of out-of-town trip recommendation. We will include illustrative examples in the revised main text to clarify that POI categories have been incorporated into our framework.\n\nW1&Q2: With respect to the PersTour method [2] mentioned by the reviewer, we acknowledge its relevance. However, due to its relatively early publication and the fact that it has been consistently outperformed by more recent methods such as GraphTrip [4], MatTrip [5], and AR-Trip [6] (which we already included as baselines), we initially did not include it in our comparison. Nevertheless, in response to the reviewer's suggestion, we have now additionally implemented and compared PersTour with our approach. The results are presented in the updated table below. The results further confirm the effectiveness of our proposed method over earlier approaches. We will also update Table 1 in the revised manuscript to include these new comparison results.\\\nAs for the MDTRP method [3] mentioned by the reviewer, we note that the authors have not released the source code, either in the paper or through public platforms such as GitHub or Kaggle, which makes it difficult to reproduce their approach within a short timeframe. However, we observed that the overall architecture of MDTRP is highly similar to that of MatTrip [5]--a baseline we have already included in our experiments. Both models adopt LSTM-based encoders and attention mechanisms, and both learn user interests over POI categories. As shown in Table 1 of our paper, our method consistently outperforms MatTrip, which indirectly demonstrates the advantage of our approach over methods in the style of MDTRP.\n\n||Foursquare||||Yelp||||\n|-|-|-|-|-|-|-|-|-|\n|**Method**|$F_1$(↑)|$PairsF_1$(↑)|$Full$-$F_1$(↑)|$Full$-$PairsF_1$(↑)|$F_1$(↑)|$PairsF_1$(↑)|$Full$-$F_1$(↑)|$Full$-$PairsF_1$(↑)|\n|PersTour [2]|0.0258|0.0016|0.4421|0.1572|0.0251|0.0066|0.5059|0.2074|\n|SPOT-Trip|**0.0400**|**0.0109**|**0.4723**|**0.1960**|**0.0399**|**0.0190**|**0.5261**|**0.2347**|\n\nQ3: Thank you for this thoughtful question. In our current task formulation, we focus on proactively generating personalized trip trajectories based on a user's historical hometown records and their issued query, which specifies a starting point, an ending point, and the desired number of intermediate POIs. While we acknowledge the importance of considering travel time and distance, not all users provide explicit budget constraints for these factors in practice. As such, our current framework does not assume such inputs, and instead recommends POI sequences based primarily on user preference modeling. Nevertheless, we agree that evaluating and optimizing tour quality in terms of diversity, total travel distance, and estimated time is a valuable future direction. In future work, we plan to incorporate additional modules to account for user-specified other constraints (e.g., time or distance budgets) and to design corresponding evaluation metrics that better capture the practical quality of generated trips.\n\nA minor typo: Thank you for pointing out this typo. We will correct it in the revised version.\n\n**Reference:**\\\n[1] Xin, Haoran, et al. \"Out-of-town recommendation with travel intention modeling.\" AAAI. 2021.\\\n[2] Lim, Kwan Hui, et al. \"Personalized tour recommendation based on user interests and points of interest visit durations.\" IJCAI. 2015.\\\n[3] Huang, Feiran, Jie Xu, and Jian Weng. \"Multi-task travel route planning with a flexible deep learning framework.\" TITS (2020).\\\n[4] Gao, Qiang, et al. \"Dual-grained human mobility learning for location-aware trip recommendation with spatial-temporal graph knowledge fusion.\" Information Fusion (2023).\\\n[5] Zhang, Jiale, et al. \"Encoder-decoder based route generation model for flexible travel recommendation.\" TSC (2024).\\\n[6] Shu, Wenzheng, et al. \"Analyzing and mitigating repetitions in trip recommendation.\" SIGIR. 2024."}, {"review_id": "wzjyoU7MoR", "reviewer": "Reviewer_Mree", "summary": "This paper proposes **SPOT-Trip**, a dual-preference (static and dynamic) recommendation framework for out-of-town travel, which addresses the challenges of data sparsity and user interest drift. Extensive experiments on real-world datasets demonstrate that SPOT-Trip significantly outperforms previous baseline models.", "questions": "1. See weakness.\n\n2. The paper claims this is a new task. Does that mean both trip recommendation and out-of-town recommendation have been studied individually, but their combination has not been explored before?\n\n3. According to Figure 1, I don’t quite understand the motivation for separating hometown check-in sequences from out-of-town ones. Doesn’t this split a complete trajectory into disjoint parts? For instance, if a user lives in the city and commutes to the suburbs for work, would those commuting behaviors be treated as part of the out-of-town trips? Or did the authors filter out such cases?\n\n4. “However, due to the irregular sampling \\[32] of check-in data, these approaches fail to capture the dynamic evolution of user preferences over actual time. Given the success of neural ODE \\[8] in other research fields \\[30, 21], we develop a neural ODE to model the continuous dynamic drift of user out-of-town preferences in latent space.” — I’m not sure I fully understand this. For instance, wouldn’t it be simpler and more intuitive to compute the time gap and append it to the input in an RNN or Transformer-based model? Why is an ODE necessary here? Is it used just because other works have used it? What exactly are the advantages of using ODEs in this context?\n\n5. From Table 1, the best performance is only around 0.04. Is this practically meaningful? In prior work on next POI prediction based on Foursquare, the typical Acc\\@1 can reach around 20%. Why didn’t the paper use accuracy as a metric? Also, why not report standard recommendation metrics such as NDCG or MRR?\n\n6. How do you distinguish between hometown and out-of-town regions? How is a user’s hometown identified? Did the authors use the Foursquare category name (e.g., “home”)?\n\n7. I'm a bit confused. Based on the statistics, the number of hometown check-ins is far greater than that of out-of-town check-ins. Does this mean traditional next POI recommendation methods are sufficient for hometown data, while a separate method must be developed specifically for out-of-town cases? Where exactly does the necessity of this distinction come from?\n\n8. I’m curious whether it is possible to visualize where SPOT-Trip underperforms—for example, in particular types of trips or specific regions. People’s behavior likely differs significantly across countries and regions.\n\n9. For trip recommendation, a natural thought is to add feasibility constraints, such as start time or economic viability of transportation. Does this mean current methods are still far from being truly applicable in real-world use cases?", "limitations": "The paper only discusses technical limitations but does not address potential negative societal impacts. Since this work requires identifying users' hometowns, I believe it could potentially pose certain negative implications for user privacy.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The motivation of the paper is reasonable.\n\n2. The methodology is described in detail.\n\n3. The case study effectively demonstrates the effectiveness of the proposed method.\n\n\nWeakness:\n\n1. The paper needs to be carefully reviewed for details. For example, symbols like **d** are not explained, and formulas such as (3) and (8) are missing punctuation marks.\n\n2. Some redundant content in Sections 1 and 3 could be streamlined. Completely removing the Related Work section from the main body is generally unacceptable. In addition, important statistics such as the size of the knowledge graph should be presented in the main text.\n\n3. The proposed method is relatively complex, and it is unclear how efficient it is computationally. Some components also seem unnecessarily complicated—for example, the *Static Aggregator* could be replaced by simple mean pooling. It's unclear why a new concept is introduced here. Furthermore, the paper lacks comparisons with some standard *out-of-town recommendation* baselines.\n\n4. I could not find the **PPROC** method mentioned in \\[21].\n\n5. There appears to be no ablation study for the **Static-Dynamic Preference Fusion** module.", "was_revised": true, "final_justification": "Only some of the concerns were resolved", "rebuttal": "Thank you for your constructive and detailed feedback on our paper. We sincerely appreciate your time and effort in reviewing our manuscript. We are grateful for your recognition of the motivation, methodology, and case study design. Below, we will address the weaknesses and questions you raised. \n\n**Response to Weaknesses**:\n\nW1: Thanks for pointing this out. We apologize for the oversight. In the revised version, we have carefully proofread the manuscript and added clear definitions for all symbols, including *d* (i.e., the dimension of the hidden representation), and corrected the punctuation in all equations, including Eq. (3) and Eq. (8).\n\nW2: Thanks for the insightful suggestions. In the revised version, we will streamline Sections 1 and 3 to remove redundant content and improve clarity. Additionally, we agree that the *Related Work* section is important and will move it from the appendix to the main text. The statistics of the knowledge graph are already provided in the appendix--we will also relocate them to the main text for better visibility.\n\nW3: Thanks for pointing this out. As you suggested, the proposed **Static Aggregator** indeed functions as a mean pooling mechanism. To avoid introducing an unnecessary new concept, we will rename it as **Mean Aggregator** in the revised version. Regarding the baseline comparison, many existing out-of-town recommendation models [1][2][3] focus on next POI prediction rather than full trip generation, and thus are not directly applicable to our task. KDDC [3] is a relatively suitable and SOTA approach among these methods, so we have adopted its knowledge-enhanced module in our **Base** framework (**Base+KDDC**) as a comparative baseline.\n\nW4: We apologize for the confusion. The paper [4] does not explicitly name its method, so we referred to it as **PPROC** based on the name used in its official GitHub repository. We will clarify this naming choice in the baseline descriptions to avoid misunderstanding.\n\nW5: In fact, removing the **Static-Dynamic Preference Fusion** module is equivalent to discarding both the **Knowledge-Enhanced Static Preference Learning** and the **ODE-Based Dynamic Preference Learning** components (i.e., the **Base** baseline reported in our paper). To address your concern, we will explicitly clarify this equivalence in the revised version.\n\n**Response to Questions**:\n\nQ2: Yes, your understanding is correct. Existing trip recommendation methods [4][5] generally do not consider user check-in behaviors in their hometown, while existing out-of-town recommendation works [1][2][3] are mostly limited to next POI prediction in an unfamiliar city. To the best of our knowledge, no prior work has addressed the generation of a full trip in an out-of-town scenario. This is the gap our work aims to fill.\n\nQ3: Thanks for the question. Our motivation for separating hometown and out-of-town check-in sequences follows prior out-of-town recommendation works [1][2][3], which emphasizes the interest drift between users' hometown preferences and their behaviors in unfamiliar cities. The goal of out-of-town recommendation is to suggest POIs that users might be interested in when traveling in out-of-town regions. Moreover, the regions in our study are defined exactly as annotated in the original datasets (Foursquare and Yelp), without any modification. For example, users whose hometown is Los Angeles and who travel to San Diego are considered to be on out-of-town travel. The case reflects actual travel behaviors rather than routine suburban commutes. Therefore, daily commuting--such as traveling from a city to nearby suburbs--is unlikely to be captured as out-of-town behavior, given the dataset's regional granularity. For more details on how we distinguish the hometown and out-of-town regions, please refer to our response to Q6.\n\nQ4: Thanks for the question. In our task, the time intervals between check-ins during a user’s out-of-town trip are irregular and unknown. Therefore, we cannot directly use actual time gaps as input. RNNs and Transformers generally assume that input sequences are equally spaced or have known time gaps. When we use step indices corresponding to the steps to be predicted (e.g., POI 1 → POI 2 → POI 3), these models may implicitly assume a constant rate of preference change, which does not reflect the true dynamics of user behavior during travel.  In contrast, our use of Neural ODEs allows the model to learn nonlinear, continuous preference transitions over a latent time dimension. This enables more flexible modeling of preference drift at variable speeds between steps. Empirically, this design also leads to better performance (see below or our response to reviewer 8nVL Q3), confirming the advantage of Neural ODEs in handling such irregular, implicit temporal dynamics. \n\n|Method|Foursquare-$F_1$|Foursquare-$PairsF_1$|Yelp-$F_1$|Yelp-$PairsF_1$|\n|-|-|-|-|-|\n|**SPOT-Trip**|**0.0400**|**0.0109**|**0.0399**|**0.0190**|\n|Base+KSPL+Transformer| 0.0362|0.0063|0.0370|0.0172|\n|Base+KSPL+GRU|0.0341|0.0070|0.0358|0.0169|\n\nQ5: Thank you for the question. The value may appear low at first glance. However, our task is fundamentally more challenging than next-POI prediction. Rather than predicting just the next check-in point, our model must generate an entire out-of-town trip and match multiple POIs in both content and exact position. This position-wise alignment across a sequence significantly increases difficulty, especially under real-world data sparsity. Despite the difficulty, compared to prior trip recommendation approaches, our method achieves a significant performance improvement under the same evaluation metrics. Regarding ACC, NDCG, and MRR using in traditional next POI prediction: These metrics usually rely on negative sampling, which are not applied in trip recommendation evaluation. More importantly, they are ranking-based and focus on whether the correct POI appears near the top of a list--not on position-wise sequence matching, which is essential in our trip recommendation task. Our goal is to assess whether the predicted sequence aligns step-by-step with the ground-truth travel trajectory. Therefore, following prior trip recommendation work [5][6], we use $F_1$ and $PairsF_1$, which better reflect sequential prediction quality.\n\nQ6&Concern about user privacy in Limitations: Thanks for the question. As mentioned in the dataset section of our paper, we follow the hometown discovery approach introduced in prior work [2] to distinguish between a user's hometown and out-of-town regions. In brief, it first extracts a user's check-in sequences across different regions over continuous time windows. Then, based on factors such as the number of check-ins and time span, it assigns a score to regions and selects the highest-scoring one as the user's hometown. Detailed examples can be found in the dataset of our released code. As for location categories, we do make use of semantic POI types from the dataset (e.g., Restaurant, Mall), but the ‘home’ does not appear in the available taxonomy and is not used for identifying hometowns. Regarding the concern about user privacy, we would like to clarify that, consistent with prior out-of-town recommendation works [1][2][3], all user data used in this study has been fully anonymized. No personally identifiable information is involved, and the hometown identification is performed at the region level based solely on anonymous user IDs and aggregated check-in behavior.\n\nQ7: As noted in our response to Q3, the motivation for treating out-of-town recommendation as a separate task lies in the drift between users’ preferences in their hometown and in unfamiliar regions. Traditional next-POI methods [7][8] typically model a user's check-ins within a single region (e.g., New York) and recommend the next POI in that same region. However, when users travel to a new region (e.g., from Los Angeles to San Diego), these models often fail to generalize due to region-specific patterns. While such methods may work for hometown prediction, they are insufficient for out-of-town scenarios, where behavior is driven more by travel preferences. Hence, distinguishing between hometown and out-of-town regions is necessary to capture this interest shift, as emphasized in prior works [1][2][3].\n\nQ8: Thank you for the suggestion. We agree that user behaviors vary across regions, and visualizing underperformance is important. In the appendix (Case Study), we show that SPOT-Trip may generate repetitive POIs in some trips, reflecting a limitation in diversity modeling. While we do not conduct a systematic regional error analysis in this work, we see this as a valuable direction for future research.\n\nQ9: Our work focuses on modeling user preferences in trip recommendation. Incorporating feasibility constraints (e.g., time, cost) is important for real-world use but typically requires extra data sources. We consider this an important direction and believe that combining preference learning with constraint-aware modeling is a promising future path.\n\n**Reference:**\\\n[1] Xin, Haoran, et al. \"Out-of-town recommendation with travel intention modeling.\" AAAI. 2021.\\\n[2] Xin, Haoran, et al. \"Captor: A crowd-aware pre-travel recommender system for out-of-town users.\" SIGIR. 2022.\\\n[3] Liu, Yinghui, et al. \"KDDC: Knowledge-driven disentangled causal metric learning for pre-travel out-of-town recommendation.\" IJCAI. 2024.\\\n[4] Iakovlev, Valerii, and Harri Lähdesmäki. \"Learning Spatiotemporal Dynamical Systems from Point Process Observations.\" ICLR, 2025.\\\n[5] Zhang, Jiale, et al. \"Encoder-decoder based route generation model for flexible travel recommendation.\" TSC (2024).\\\n[6] Shu, Wenzheng, et al. \"Analyzing and mitigating repetitions in trip recommendation.\" SIGIR. 2024.\\\n[7] Qin, Yifang, et al. \"A diffusion model for POI recommendation.\" TOIS (2023).\\\n[8] Zhou, Hailun, et al. \"Disentangled Graph Debiasing for Next POI Recommendation.\" SIGIR. 2025."}, {"review_id": "lVt6kQam09", "reviewer": "Reviewer_XSsn", "summary": "The work proposes SPOT-Trip, a novel framework for out-of-town trip recommendation, whose goal is to generate personalized POI trajectories for users traveling from their hometowns to unfamiliar regions with contextual constraints such as origin, destination, and trip duration. The paper first identifies two major challenges: the sparsity of out-of-town check-in data and the dual nature of user preferences—comprising both static and dynamic interests. To mitigate the data sparsity and model static preferences, a POI attribute knowledge graph is constructed, enabling relation-aware aggregation. To capture dynamic and time-evolving preferences, SPOT-Trip leverages neural ODEs and temporal point processes. Finally, a static-dynamic fusion module is introduced to integrate both aspects of user preference for effective recommendation.", "questions": "Q1.Could the authors provide more detailed information about the datasets used, particularly the time span of the Yelp dataset, to help readers better understand and preprocess the raw data?\n\nQ2.Is it possible to extend the SPOT-Trip framework to generate top‑K candidate trajectories, which may help enhance user choice and recommendation diversity?", "limitations": "Yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nS1.The paper addresses a practical and unexplored problem, out-of-town trip recommendation, with clear consideration of real-world constraints such as origin, destination, and trip duration, which has meaningful implications for enhancing user travel planning. \n\nS2.The proposed SPOT-Trip framework is technically sound and novel. It introduces several innovative components, including a POI attribute knowledge graph for static preferences and neural ODEs with temporal point processes for modeling dynamic preferences.\n\nS3.The manuscript is well-written and logically organized, which facilitates understanding. \n\nS4.The experiments are comprehensive and well-structured, covering five different types of evaluations that assess various aspects of the proposed method. Notably, the case study is presented in an intuitive and visually engaging manner, effectively demonstrating the practical benefits of the model. The implementation details are clearly described to ensure reproducibility.\n\n\\\nWeaknesses:\n\nW1.The authors employ different activation functions (e.g., LeakyReLU in Eqs. (1) and (10) and SiLU in Eq. (2)), but the reason behind these choices is not clearly explained. It is recommended that the authors provide further justification or empirical evidence to support the use of these specific activations.\n\nW2.Some redundant statements exist, which are suggested to be corrected. For instance, lines 599-600 contain redundant descriptions regarding baseline methods. It would be better to conduct a thorough proofreading of the entire manuscript to eliminate such errors.\n\nW3.More recent related work are suggested to be included as follows [1,2,3].\n\n[1] Evidential stochastic differential equations for time-aware sequential recommendation. NeurIPS 2024.\n\n[2] Unleashing the power of knowledge graph for recommendation via invariant learning. WWW 2024.\n\n[3] DR-VAE: Debiased and Representation-enhanced Variational Autoencoder for Collaborative Recommendation. AAAI 2025.", "was_revised": true, "final_justification": "The authors have satisfactorily addressed my concerns.", "rebuttal": "Thank you for your thoughtful and encouraging review. We sincerely appreciate your recognition of the novelty, technical soundness, and practical relevance of our work, as well as your positive comments on the manuscript’s clarity, experimental rigor, and reproducibility. We also thank you for your valuable time and effort in reviewing our paper. Below, we carefully address the weaknesses and questions you raised.\n\n**Response to Weaknesses**:\nW1: Thank you for your suggestion. We have added a replacement-based ablation study to clarify the activation function choices. Specifically, we denote the activations in Eqs. (1), (2), and (10) as $\\phi_{(1)}$, $\\phi_{(2)}$, and $\\phi_{(10)}$, and conduct exhaustive experiments by replacing each with LeakyReLU, SiLU, or GELU. The results are summarized in the three tables provided. Although some alternative combinations slightly outperform our original settings on individual metrics, our chosen configuration still achieves consistently strong performance across both datasets and metrics. Therefore, we retain the original setup.\n\n$\\phi\\_{(1)}$ == LeakyReLU:\n|$\\phi\\_{(2)}$|$\\phi\\_{(10)}$|Foursquare-$F_1$|Foursquare-$PairsF_1$|Yelp-$F_1$|Yelp-$PairsF_1$|\n|-|-|-|-|-|-|\n|LeakyReLU|LeakyReLU|0.0386|0.0094|0.0378|0.0175|\n|LeakyReLU|SiLU|0.0391|0.0101|0.0384|0.0181|\n|LeakyReLU|GELU|**0.0407**|0.0097|0.0391|0.0178|\n|SiLU|LeakyReLU|0.0400|**0.0109**|**0.0399**|0.0190|\n|SiLU|SiLU|0.0397|0.0105|0.0394|0.0182|\n|SiLU|GELU|0.0391|0.0103|0.0389|0.0179|\n|GELU|LeakyReLU|0.0381|0.0085|0.0387|0.0193|\n|GELU|SiLU|0.0387|0.0100|0.0391|0.0181|\n|GELU|GELU|0.0381|0.0097|0.0392|0.0185|\n\n$\\phi\\_{(1)}$ == SiLU:\n|$\\phi\\_{(2)}$|$\\phi\\_{(10)}$|Foursquare-$F_1$|Foursquare-$PairsF_1$|Yelp-$F_1$|Yelp-$PairsF_1$|\n|-|-|-|-|-|-|\n|LeakyReLU|LeakyReLU|0.0378|0.0095|0.0381|0.0187|\n|LeakyReLU|SiLU|0.0389|0.0101|0.0394|0.0185|\n|LeakyReLU|GELU|0.0389|0.0104|0.0398|0.0181|\n|SiLU|LeakyReLU|0.0402|0.0107|0.0380|0.0184|\n|SiLU|SiLU|0.0401|0.0103|0.0389|0.0181|\n|SiLU|GELU|0.0392|0.0108|0.0379|0.0178|\n|GELU|LeakyReLU|0.0384|0.0102|0.0385|0.0184|\n|GELU|SiLU|0.0397|0.0103|0.0381|0.0175|\n|GELU|GELU|0.0391|0.0103|0.0394|0.0191|\n\n$\\phi\\_{(1)}$ == GELU:\n|$\\phi\\_{(2)}$|$\\phi\\_{(10)}$|Foursquare-$F_1$|Foursquare-$PairsF_1$|Yelp-$F_1$|Yelp-$PairsF_1$|\n|-|-|-|-|-|-|\n|LeakyReLU|LeakyReLU|0.0395|0.0108|0.0391|0.0179|\n|LeakyReLU|SiLU|0.0381|0.0103|0.0394|0.0187|\n|LeakyReLU|GELU|0.0394|0.0101|0.0397|**0.0195**|\n|SiLU|LeakyReLU|0.0384|0.0107|0.0378|0.0178|\n|SiLU|SiLU|0.0389|0.0102|0.0394|0.0184|\n|SiLU|GELU|0.0394|0.0095|0.0388|0.0190|\n|GELU|LeakyReLU|0.0381|0.0084|0.0398|0.0185|\n|GELU|SiLU|0.0375|0.0098|0.0387|0.0183|\n|GELU|GELU|0.0391|0.0105|0.0391|0.0188|\n\nWe will include this new set of results and corresponding discussion in the revised appendix.\n\nW2: Thank you for pointing this out. We will revise the mentioned lines and carefully proofread the entire manuscript to eliminate redundant statements and improve clarity.\n\nW3: Thank you for the helpful suggestions. We acknowledge that the suggested recent works exhibit partial similarities with components of our framework. We will include these works [1–3] in the revised version and elaborate on their connections and distinctions with our method in the related work section.\n\n**Response to Questions**:\n\nQ1: Thank you for the valuable suggestion. The Yelp dataset covers data up to the year 2021, and the timestamps in the dataset (e.g., 1559768993) are recorded as Unix epoch time in seconds, which can be converted to standard date-time format for preprocessing. To improve clarity for readers, we will include these detailed descriptions in the revised manuscript.\n\nQ2: Thank you for the insightful suggestion. Extending the SPOT-Trip framework to generate top-K candidate trajectories would indeed require incorporating appropriate search and trajectory scoring strategies to balance diversity and relevance. We consider this an important direction for future work and plan to explore it in subsequent research."}]

Author rebuttal:

Meta-review: This paper proposes a new task called out-of-town trip recommendation and presents a novel model, SPOT-Trip, to solve it. The authors clearly explain how their framework uses both static and dynamic user preferences. They model static preferences with a knowledge graph and dynamic preferences with neural ODEs and temporal point processes. Reviewer XSsn strongly supports the paper and considers the problem important and the technical solution well designed. The experiments are well done, and the writing is clear. This reviewer finds the paper a strong contribution.

Other reviewers raised concerns about novelty and metrics. The authors responded in detail and tried to clarify the differences from past work. They explained why some standard metrics are not suitable in their setup and also added new baselines. While not all reviewers changed their scores, I think the paper brings a meaningful contribution to the trip recommendation area. It introduces a new problem, uses a solid framework, and offers practical value. For these reasons, I recommend acceptance.
Decision: Accept (poster)