Relation Embedding for Personalised POI Recommendation

Publication Year: 2020 Publication Type : JournalArticle


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.


@article{DBLP:journals/corr/abs-2002-03461, archiveprefix = {arXiv},
    author = {Xianjing Wang and Flora D. Salim and Yongli Ren and Piotr Koniusz},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    eprint = {2002.03461},
    journal = {CoRR},
    timestamp = {Sun, 01 Mar 2020 00:00:00 +0100},
    title = {Relation Embedding for Personalised POI Recommendation},
    url = {},
    volume = {abs/2002.03461},
    year = {2020}


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