Enhancing POI Recommendation through Global Graph Disentanglement with POI Weighted Module

• URL: http://arxiv.org/abs/2507.14612v1
• Date: Sat, 19 Jul 2025 13:16:44 GMT
• Title: Enhancing POI Recommendation through Global Graph Disentanglement with POI Weighted Module
• Authors: Pei-Xuan Li, Wei-Yun Liang, Fandel Lin, Hsun-Ping Hsieh,
• Abstract summary: Next point of interest (POI) recommendation primarily predicts future activities based on users' past check-in data and current status.<n>This paper proposes a novel next POI recommendation framework called Graph Disentangler with POI Weighted Module (GDPW)
• Score: 4.736059095502583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next point of interest (POI) recommendation primarily predicts future activities based on users' past check-in data and current status, providing significant value to users and service providers. We observed that the popular check-in times for different POI categories vary. For example, coffee shops are crowded in the afternoon because people like to have coffee to refresh after meals, while bars are busy late at night. However, existing methods rarely explore the relationship between POI categories and time, which may result in the model being unable to fully learn users' tendencies to visit certain POI categories at different times. Additionally, existing methods for modeling time information often convert it into time embeddings or calculate the time interval and incorporate it into the model, making it difficult to capture the continuity of time. Finally, during POI prediction, various weighting information is often ignored, such as the popularity of each POI, the transition relationships between POIs, and the distances between POIs, leading to suboptimal performance. To address these issues, this paper proposes a novel next POI recommendation framework called Graph Disentangler with POI Weighted Module (GDPW). This framework aims to jointly consider POI category information and multiple POI weighting factors. Specifically, the proposed GDPW learns category and time representations through the Global Category Graph and the Global Category-Time Graph. Then, we disentangle category and time information through contrastive learning. After prediction, the final POI recommendation for users is obtained by weighting the prediction results based on the transition weights and distance relationships between POIs. We conducted experiments on two real-world datasets, and the results demonstrate that the proposed GDPW outperforms other existing models, improving performance by 3% to 11%.

Related papers

• LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization [58.65395773049273]
  Location Preference Optimization (LPO) is a novel approach that leverages locational data to optimize interaction preferences.<n>LPO uses information entropy to predict interaction positions by focusing on zones rich in information.<n>Our code will be made publicly available soon, at https://github.com/AIDC-AI/LPO.
  arXiv  Detail & Related papers  (2025-06-11T03:43:30Z)
• Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors [7.294418916091012]
  Machine learning models can predict Points of Interest (POIs) that individuals are likely to visit in the future by analyzing their historical visit patterns. Previous studies address this problem by learning a POI classifier, where each class corresponds to a POI. We propose a model designed to predict a new POI outside the training data as long as its context is aligned with the user's interests.
  arXiv  Detail & Related papers  (2024-11-22T16:50:10Z)
• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms.<n>Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction.<n>We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation [81.4999547454189]
  Existing works assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. In real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes. We propose a Bayes-enhanced Multi-view Attention Network to address the uncertainty factors of the user check-ins.
  arXiv  Detail & Related papers  (2023-11-01T12:47:38Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
  We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
  arXiv  Detail & Related papers  (2022-06-06T15:42:54Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification [37.025295828186955]
  We propose a novel neural network approach to identify the missing POI categories. Specifically, we design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.
  arXiv  Detail & Related papers  (2021-12-31T04:15:37Z)
• Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification [38.51964956686177]
  We develop a model, named Bi-STDDP, which can integrate bi-directional-temporal dependence and users' dynamic preferences. Results demonstrate significant improvements of our model compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-31T03:54:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.