Abstract
Recommender system (RS) is a web personalization tool for recommending appropriate items to users based on their preferences from a large set of available items. Collaborative filtering (CF) is the most popular technique for recommending items based on the preferences of similar users. Most of the CF based RSs work only on the overall rating of the items, however, the overall rating is not a good representative of user preferences for an item. Our work in this paper, is an attempt towards incorporating of various criteria ratings into CF i.e., multi-criteria CF, for enhancing its accuracy through multi-linear regression. We suggest the use of multi-linear regression for determining the weights of individual criterion and computing the overall ratings of each item. Experimental results reveal that the proposed approach outperforms the classical approaches.
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Jhalani, T., Kant, V., Dwivedi, P. (2016). A Linear Regression Approach to Multi-criteria Recommender System. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_23
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DOI: https://doi.org/10.1007/978-3-319-40973-3_23
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