Abstract
In the area of recommender systems, collaborative filtering is widely used technique for recommending appropriate items to a user based on the available ratings given by similar users. Most recommender systems (RSs) work only on the single criterion rating i.e., overall rating, however overall rating may not be a good representative of a user preference. Single criterion collaborative filtering (CF) does not generate more reliable recommendations because it suffers from correlation based similarity problems. Moreover, representation of uncertain user preferences is another concern of CF. In our work, we develop a novel fuzzy Bayesian approach to multi-criteria CF for handling uncertain user preferences and correlation based similarity problems. Further, incorporation of multi-criteria ratings into CF would be helpful for generating effective recommendations. Through experiments on Yahoo! Movies dataset, we compare our proposed approach to baseline approaches and demonstrate its effectiveness in terms of accuracy, recall and f-measure.
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Kant, V., Jhalani, T. & Dwivedi, P. Enhanced multi-criteria recommender system based on fuzzy Bayesian approach. Multimed Tools Appl 77, 12935–12953 (2018). https://doi.org/10.1007/s11042-017-4924-2
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DOI: https://doi.org/10.1007/s11042-017-4924-2