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

TL;DR

This paper introduces a neural network model that leverages bi-directional transition patterns and personal preferences to accurately identify missing POI categories in check-in data, outperforming existing methods.

Contribution

The paper proposes a novel neural network with an attention matching cell that integrates global transition patterns and personal preferences for missing POI category identification.

Findings

Model outperforms state-of-the-art baselines on real-world datasets.

Effective in identifying missing POI categories at any time.

Can be extended to next POI category recommendation tasks.

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.