Abstract
In some recommender systems where users can vote objects by ratings, the similarity between users can be quantified by a benchmark index, namely the Pearson correlation coefficient, which reflects the rating correlations. Another alternative way is to calculate the similarity based solely on the relevance information, namely whether a user has voted an object. The former one uses more information than the latter, and is intuitively expected to give more accurate rating predictions under the standard collaborative filtering framework. However, according to the extensive experimental analysis, this letter reports the opposite results that the latter method, making use of only the relevance information, can outperform the former method, especially when the data set is sparse. Our finding challenges the routine knowledge on information filtering, and suggests some alternatives to address the sparsity problem.