Online Recommender System Considering Changes in User's Preference

Recommender systems extract unseen information for predicting the next preferences. Most of these systems use additional information such as demographic data and previous users' ratings to predict users' preferences but rarely have used sequential information. In streaming recommender systems, the emergence of new patterns or disappearance a pattern leads to inconsistencies. However, these changes are common issues due to the user's preferences variations on items. Recommender systems without considering inconsistencies will suffer poor performance. Thereby, the present paper is devoted to a new fuzzy rough set-based method for managing in a flexible and adaptable way. Evaluations have been conducted on twelve real-world data sets by the leave-one-out cross-validation method. The results of the experiments have been compared with the other five methods, which show the superiority of the proposed method in terms of accuracy, precision, recall.

Keywords:

Recommender Systems , Online Learning , Natural Noise , Concept Drift