Enhanced multi-criteria recommender system based on fuzzy Bayesian approach

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 ratin...

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Bibliographic Details
Published in:Multimedia tools and applications 2018-05, Vol.77 (10), p.12935-12953
Main Authors: Kant, Vibhor, Jhalani, Tanisha, Dwivedi, Pragya
Format: Article
Language:English
Subjects:
Bayesian analysis
Collaboration
Computer Communication Networks
Computer Science
Criteria
Data Structures and Information Theory
Filtration
Fuzzy systems
Multimedia Information Systems
Ratings
Recommender systems
Similarity
Special Purpose and Application-Based Systems
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Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-4924-2