SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation

2025-06-02

AI Summary

To address the sparsity of check-in data and the challenge of jointly modeling long-term user preferences and dynamically evolving interests during out-of-town travel, this paper proposes the first static–dynamic preference disentanglement framework for out-of-town POI sequence recommendation. Methodologically, it integrates a POI knowledge graph for attribute-aware representation learning, employs neural ordinary differential equations (ODEs) to model the continuous evolution of user interests, and leverages temporal point processes to capture the sequential dependencies among check-in events; it further designs a collaborative fusion mechanism for static and dynamic preferences. Experiments on real-world datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches, achieving up to a 17.01% improvement in recommendation accuracy. This work establishes a novel paradigm for personalized travel recommendation under data-sparse scenarios.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

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%.

Problem

• Modeling dual static-dynamic user preferences for trip recommendations
• Addressing data sparsity in out-of-town check-in records
• Separating and fusing static and dynamic preferences effectively

Innovation

• POI attribute knowledge graph enriches semantic modeling
• Neural ODEs capture dynamic preference evolution
• Static-dynamic fusion module merges dual preferences