Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach

Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach

Huayu Li, Yong Ge, Defu Lian, Hao Liu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2117-2123. https://doi.org/10.24963/ijcai.2017/294

Point-of-Interest (POI) recommendation has been an important service on location-based social networks. However, it is very challenging to generate accurate recommendations due to the complex nature of user's interest in POI and the data sparseness. In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user's interest, and adaptively model the missing data. Specifically, a user's general interest in POI is modeled as a mixture of her intrinsic and extrinsic interests, upon which we formulate the ranking constraints in our unified recommendation approach. Furthermore, a self-adaptive location-oriented method is proposed to capture the inherent property of missing data, which is formulated as squared error based loss in our unified optimization objective. Extensive experiments on real-world datasets demonstrate the effectiveness and advantage of our approach.
Keywords:
Machine Learning: Data Mining
Multidisciplinary Topics and Applications: Personalization and User Modeling