Relation Embedding for Personalised POI Recommendation

• URL: http://arxiv.org/abs/2002.03461v2
• Date: Wed, 19 Feb 2020 16:40:48 GMT
• Title: Relation Embedding for Personalised POI Recommendation
• Authors: Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz
• Abstract summary: We propose a translation-based embedding for POI recommendation. Our approach encodes the temporal and semantic contents effectively in a low-temporal relation space. A combined factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests.
• Score: 34.043989803855844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.

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