Enhanced multi-criteria recommender system based on fuzzy Bayesian approach
| Autor: | Vibhor Kant, Pragya Dwivedi, Tanisha Jhalani |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science RSS Fuzzy set Bayesian probability 02 engineering and technology computer.file_format Recommender system Machine learning computer.software_genre Fuzzy logic Naive Bayes classifier Hardware and Architecture 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Media Technology Collaborative filtering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Software |
| Zdroj: | Multimedia Tools and Applications. 77:12935-12953 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-017-4924-2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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