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, https://dblp.org},
biburl = {https://dblp.org/rec/journals/corr/abs-2002-03461.bib},
eprint = {2002.03461},
journal = {CoRR},
timestamp = {Sun, 01 Mar 2020 00:00:00 +0100},
title = {Relation Embedding for Personalised POI Recommendation},
url = {https://arxiv.org/abs/2002.03461},
volume = {abs/2002.03461},
year = {2020}
}
© 2021 Flora Salim - CRUISE Research Group.