Title: SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Out-of-town trip recommendation; Static-dynamic user preference; Ordinary differential equation
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%.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nQ1.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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nCan 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?\n\nLIMITATIONS:\nPlease see suggestions in questions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 1, "DATE": "2025-07-02"}