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Collaborative filtering aims to predict a person’s preferences by examining the preferences of similar people. Many collaborative filtering algorithms rely on a coarse notion of similarity, which assumes that if two people are sufficiently simiar in a few specific areas, each is likely to make good recommendations for the other in most areas. Our trust in the opinions of others, though, is rarely absolute; we often tend to trust recommendations from certain people in certain areas. In this paper we develop an algorithm which reflects this notion. Rather than capturing taste information at the user level, we capture taste at the topic level by making use of tags: arbitrary words or phrases which are often used to group online content. Previous attempts to improve collaborative filtering using tag information have attempted to determine tag meanings, and as a result have depended upon complex semantic analyses. Our algorithm avoids these complications by focusing instead on the clusters which tags establish. Using tags in this way provides a significant improvement in the accuracy of recommendations without a commensurate loss in coverage. These tag clusters also give rise to networks which can be exploited to further improve recommendation results.

I’m Like You, Just Not In That Way: Tag Networks to Improve Collaborative Filtering