Abstract
Recommender systems are extensively utilized on the internet for helping customers to pick on the items which strike his fancy. Along with the fast progress of online social networks, how to use the additional social information for recommendation has been intensively investigated. In this article, we devise a graph embedding technology to incorporate the customers’ social network side information into conventional matrix factorization model. More specifically, first we introduce the graph embedding approach Node2Vec to obtain the customer social latent factor. Then we utilize the matrix factorization technique to find the customer scoring latent factor. Finally we think of recommendation problem as a successive task of social network embedding and integrate customer social latent factor and customer scoring latent factor into our recommendation model. We select the dominant scoring predict task as the evaluation scenario. The effectiveness for our proposed social recommendation (GSNESR) model is validated on three benchmark real world datasets. Experimental results indicate that our proposed GSNESR outperform other state-of-the-art methods.
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Acknowledgements
This work was supported by the Science Foundation of China University of Petroleum, Beijing (No. 2462020YXZZ023).
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Xiao, Bb., Liu, Jw. (2021). GSNESR: A Global Social Network Embedding Approach for Social Recommendation. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_18
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