Collaborative Filtering is the most commonly used technique in Recommender Systems, based on the users ratings in order to identify similar profiles and suggest them items. However, because it depends essentially on direct similarity measures between users or items, it usually suffers from the sparsity problem. Upon this situation, a good alternative is using global similarities to enrich the users neighborhood by transitively connecting them together, even when they do not share any common ratings. In this paper, we investigated the use of both local and global similarity measures with the maximin distance algorithm, along with tie-breaking criteria for neighbors with equal similarity. Our experiments showed that the maximin distance algorithm in fact produces many equally similar global neighbors, and that the criteria set for deciding between them severely improved the results of the recommendation process.
展开▼
