Relation Embedding for Personalised Translation-Based POI Recommendation

Publication Year: 2020 Publication Type : ConferenceProceeding


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 vary- ing spatio-temporal context pose challenges for POI systems, which af- fects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our ap- proach encodes the temporal and geographic information, as well as se- mantic contents e ectively 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 per- sonal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the e ectiveness of our proposed model.


    author = {Xianjing Wang and Flora D. Salim and Yongli Ren and Piotr Koniusz},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings, Part I},
    doi = {10.1007/978-3-030-47426-3_5},
    editor = {Hady W. Lauw and Raymond Chi-Wing Wong and Alexandros Ntoulas and Ee-Peng Lim and See-Kiong Ng and Sinno Jialin Pan},
    pages = {53--64},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Fri, 22 May 2020 01:00:00 +0200},
    title = {Relation Embedding for Personalised Translation-Based POI Recommendation},
    url = {},
    volume = {12084},
    year = {2020}


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