Fuzzy user-interest drift detection based recommender systems
- Publication Type:
- Conference Proceeding
- Citation:
- 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, pp. 1274 - 1281
- Issue Date:
- 2016-11-07
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| 07737835.pdf | Published version | 312.19 kB |
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© 2016 IEEE. Recommender systems aim to provide personalized suggestions to users by modeling user-interests to deal with information overload problem, which is extremely severe in the era of big data. Since user-interests are drifting due to their taste variation on items, recommender systems without considering that will suffer degradation of prediction accuracy. There are two challenges about adapting to user-interest drift in recommender systems: 1) accurately modeling user-interests is not easy since the drift of user-interests may occur in different direction for each user; 2) item features and user-interests are often incomplete and vague, which makes it more difficult to model user-interests. To handle these two issues, this study proposes a fuzzy user-interest drift detection based recommender system that adapts to user-interest drift and improves prediction accuracy. A fuzzy user-interest consistency model is built based on fuzzy set theories, and a user-interest drift detection approach and algorithms are developed based on concept drift techniques to provide guidance to recommendation generation. Empirical experiments are conducted on synthetic and real-world MovieLens datasets. The results show that the proposed approach improves the performance of recommender systems in metric of MAE.
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