Abstract
Recently, it is difficulty for ordinary users to find their own points of interest when facing of massive information accompanied by the popularity and development of social networks. Recommendation system is considered to be the most potential way to solve the problem by profiling personalized interest model and initiatively pushing potential interesting contents to each user. However, collaborative filtering, one of the most mature and extensively applied recommender methods currently, is facing problems of data sparsity and diversity and so on, causing its effect unsatisfactory. In the article, we put forward DLRRS, a new recommendation system depending on double linear regression models. Compared with the traditional methods, such as item average scores, collaborative filtering, and rating frequency, DLRRS has the best predictive RMSE accuracy and less fluctuation. DLRRS also has high real-time performance, which makes the system complete all the calculations in the time of Ω(n).
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This research is funded by National Key Research & Development Plan of China under Grant 2016YFB0801200 and 2016QY05X1000.
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Li, C., Wang, Z., Cao, S., He, L. (2018). DLRRS: A New Recommendation System Based on Double Linear Regression Models. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_29
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DOI: https://doi.org/10.1007/978-3-319-73317-3_29
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