Abstract
With the increasing demand for personalized recommendation, traditional collaborative filtering cannot satisfy users’ needs. Social behaviors such as tags, comments and likes are becoming more and more popular among the recommender system users, and are attracting the attentions of the researchers in this domain. The behavior characteristics can be integrated with traditional interest community and some content features. In this paper, we put forward a hybrid recommendation approach that combines social behaviors, the genres of movies and existing collaborative filtering algorithms to perform movie recommendation. The experiments with MovieLens dataset show the advantage of our proposed method comparing to the benchmark method in terms of recommendation accuracy.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Project No. 71701134), The Humanity and Social Science Youth Foundation of Ministry of Education of China (Project No. 16YJC630153), and Natural Science Foundation of Guangdong Province of China (Project No. 2017A030310427).
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Yang, C., Chen, X., Liu, L., Liu, T., Geng, S. (2018). A Hybrid Movie Recommendation Method Based on Social Similarity and Item Attributes. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_26
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DOI: https://doi.org/10.1007/978-3-319-93818-9_26
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