A novel approach to compute similarities and its application to item recommendation

Several key applications like recommender systems deal with data in the form of ratings made by users on items. In such applications, one of the most crucial tasks is to find users that share common interests, or items with similar characteristics. Assessing the similarity between users or items has several valuable uses, among which are the recommendation of new items, the discovery of groups of like-minded individuals, and the automated categorization of items. It has been recognized that popular methods to compute similarities, based on correlation, are not suitable for this task when the rating data is sparse. This paper presents a novel approach, based on the SimRank algorithm, to compute similarity values when ratings are limited. Unlike correlation-based methods, which only consider user ratings for common items, this approach uses all the available ratings, allowing it to compute meaningful similarities. To evaluate the usefulness of this approach, we test it on the problem of predicting the ratings of users for movies and jokes.