Abstract
Point of interest (POI) recommendation is one of the most important tasks in location-based social networks (LBSN). The existing recommendation methods face two challenges: (1) the cold start problem caused by data sparsity; (2) underutilization of the abundant side information besides user-POI interaction in large-scale data. Recent research shows that a user’s social relationship can be used to solve the cold start problem to some extent. The deep neural network learns users’ long term and short term preferences to improve the recommendation quality. Therefore, this paper proposes a POI recommendation model called SSANet, applying side information (S) and self-attention (SA) to provide the high-satisfaction POI recommendations for users. Specifically, first, the user-POI interaction matrix were constructed by users history data to represents the user hidden representation; second, the side information includes rating scores, access frequency, social relationship, and geographic information were used to extract users preference; third, we use self-attention mechanism to learn user long term and short term preference. The experimental results on the real LBSN datasets show that the recommendation performance of the SSANet model is better than the existing POI recommendation model.
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Yue, C., Zhu, J., Zhang, S., Ma, X. (2020). POI Recommendations Using Self-attention Based on Side Information. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_5
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DOI: https://doi.org/10.1007/978-981-15-7984-4_5
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