Abstract
Cold-start problem and sparse, long-tailed datasets are inevitable issues in recommendation systems. The solution to these problems is not to predict them in isolation, but to exploit the additional information from relevant activities. Hence recent sequential actions and social relationships of the user can be used to improve the effectiveness of the model. In this paper, we develop a novel approach called Socially-aware and sequential embedding (SASE) to fill the gap by leveraging convolutional filters to capture the sequential pattern and learning the individual social features from the social networks simultaneously. The core idea is to determine which item is relevant to the user’s historical actions and seek who is the user’s intimate friend, then make predictions based on these signals. Experimental results on several real-world datasets verify the superiority of our approach compared with various state-of-the-art baselines when handling the cold-start issues.
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Acknowledgement
This work was supported by NSFC (grant No. 61877051), CSTC (grant No. cstc2018jscx-msyb1042, cstc2017zdcy-zdyf0366 and cstc2017rgzn-zdyf0064). Li Li is the corresponding author for the paper.
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Huang, K., Cao, Y., Du, Y., Li, L., Liu, L., Liao, J. (2019). Social-Aware and Sequential Embedding for Cold-Start Recommendation. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_6
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