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arXiv · 2002.03461

Relation Embedding for Personalised POI Recommendation

Xianjing Wang·Flora D. Salim·Yongli Ren·Piotr Koniusz
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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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Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz. 2020-02-09. Relation Embedding for Personalised POI Recommendation. https://doi.org/10.1007/978-3-030-47426-3_5

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