Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification

• URL: http://arxiv.org/abs/2201.00014v1
• Date: Fri, 31 Dec 2021 04:15:37 GMT
• Title: Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification
• Authors: Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Hengshu Zhu, Pengpeng Zhao, Chang Tan, Qing He
• Abstract summary: 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.
• Score: 37.025295828186955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.

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