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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