CogTime_RMF: regularized matrix factorization with drifting cognition degree for collaborative filtering

Abstract

Due to the exponential growth of information, recommender systems have been a widely exploited technique to solve the problem of information overload effectively. Collaborative filtering (CF) is the most successful and extensively employed recommendation approach. However, current CF methods recommend suitable items for users mainly by user-item matrix that contains the individual preference of users for items in a collection. So these methods suffer from such problems as the sparsity of the available data and low accuracy in predictions. To address these issues, borrowing the idea of cognition degree from cognitive psychology and employing the regularized matrix factorization (RMF) as the basic model, we propose a novel drifting cognition degree-based RMF collaborative filtering method named CogTime_RMF that incorporates both user-item matrix and users' drifting cognition degree with time. Moreover, we conduct experiments on the real datasets MovieLens 1 M and MovieLens 100 k, and the method is compared with three similarity based methods and three other latest matrix factorization based methods. Empirical results demonstrate that our proposal can yield better performance over other methods in accuracy of recommendation. In addition, results show that CogTime_RMF can alleviate the data sparsity, particularly in the circumstance that few ratings are observed.