On evaluating the effectiveness of rating similarity-based trust

Trust metrics based on rating similarity are currently being employed to augment collaborative filtering in recommender systems with a view to mitigate the problem of data sparsity and improve their performance. Surprisingly, seldom little has been done to analyze the correlation between trust and rating similarity. In this paper, we examine the relationship between rating similarity-based implicit trust and trust that is explicitly expressed by users. We develop a logistic regression model to predict the likelihood of trust between users based on rating similarity between them. We use the large, publicly available Epinion dataset to derive the best fitting model parameters and validate the model. An empirical analysis of the relationship between rating similarity and explicit trust reveals a positive albeit very weak correlation between them. Further, we conduct experiments on implicit similarity trust prediction for varying values of similarity threshold and co-rated item count threshold. In general, results indicate low values of precision, recall, and coverage over the entire range of similarity threshold. The best precision that was obtained was 23.8 %, but with a coverage of only 0.6 % users. Best coverage value was 19.7 % but with precision of 3.2 % only. In fact, better precision can be obtained by predicting trust only on the basis of number of co-rated items without considering rating similarity between users. We therefore conclude that implicit trust based on rating similarity is not a reliable representation of trust between users.