Improved collaborative filtering algorithm using intuitionistic fuzzy C -means clustering and incorporating time factors

Abstract Collaborative filtering (CF) has been widely used to help users discover items of interest. Most of the existing CF methods collect preference information from other users to predict user’s ratings. However, the user’s interest will change over time, and the traditional collaborative filtering recommendation does not take the change of user’s interest into consideration. In addition, there are also problems of information overload and data sparseness in applications. In order to solve the problem of data sparseness and take the changes of user interests into account , we propose an improved collaborative filtering algorithm using intuitionistic fuzzy C-means clustering and incorporating time factors. Firstly, we introduce intuitionistic fuzzy C-means clustering to complete the classification of items; secondly, the above classification results are combined with the user-item score matrix to obtain the user-type preference matrix; thirdly, the joint similarities are used to obtain the nearest neighbors of the target user; finally, the changes of user interest over time are taken into account when predicting the target user’s rating on unrated items, top-k items are selected as recommendation results. To verify the effectiveness of the algorithm, experiments are conducted on Movie Lens and Booking-Crossing datasets. The Improved collaborative filtering algorithm using intuitionistic fuzzy C-means clustering results show that compared with the traditional recommendation algorithms , the algorithm proposed in this paper can effectively solve data sparseness and improve the quality and accuracy of recommendation.