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Amalgamating social media data and movie recommendation

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Abstract

Recommender systems (RSs) have become very common recently. However, RS techniques need large amounts of user and product data, which hinders RS usage for businesses with insufficient data. The RS cold-start problem may be mitigated by leveraging external data sources. We demonstrate the feasibility of solving the cold-start problem by implementing a hybrid RS that integrates the Facebook Fan Page data and the genre-classifications data from Yahoo! Movies. Our study amalgamates social media data and machine learning to build a hybrid-filtering RS. We also compared our system with three existing movie RSs—those used by Netflix, YouTube, and Amazon. Within the framework of a hybrid-filtering RS, content-based filtering was used to extract data from Yahoo! Movies and Facebook Fan Pages. The proposed RS overcame the cold-start problem and achieved a satisfactory level of accuracy.

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APA

Lee, M. R., Chen, T. T., & Cai, Y. S. (2016). Amalgamating social media data and movie recommendation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9806 LNCS, pp. 141–152). Springer Verlag. https://doi.org/10.1007/978-3-319-42706-5_11

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