Online Recommender System Considering Changes in User's Preference

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