A Novel Trust Computation Method Based on User Ratings to Improve the Recommendation

Abstract

Today, the trust has turned into one of the most beneficial solutions to improve recommender systems, especially in the collaborative filtering method. However, trust statements suffer from a number of shortcomings, including the trust statements sparsity, users' inability to express explicit trust for other users in most of the existing applications, etc. Thus to overcome these problems, this work presents a method for computing implicit trust based on user ratings, in which four influential factors including Similarity, Confidence, Analogous Opinion, and Distance are utilized to achieve trust. For computing users' similarity, the Pearson Correlation Coefficient measure was applied. Confidence was computed through users' common in items rated. To compute users' analogous opinions, what rating they have given to items was analyzed in three aspects of their satisfaction, dissatisfaction, and indifference about the items. Euclidean distance was employed on users' ratings for computing the distance. Finally, the factors were combined to reach implicit trust. Moreover, fuzzy c-means clustering was applied to initially partition similar users for enhancing the performance positively. Finally, two MovieLens datasets of 100K and 1M have used to evaluate this approach, and results have shown that the approach significantly increases Accuracy, Precision, and Recall, compared to some other methods.