Using Tag Co-occurrence for Recommendation

Tagging with free form tags is becoming an increasingly important indexing mechanism. However, free form tags have characteristics that require special treatment when used for searching or recommendation because they show much more variation than controlled keywords. In this paper we present a method that puts this large variation to good use. We introduce second order co-occurrence and a related distance measure measure for tag similarities that is robust against the variation in tags. From this distance measure it is straightforward to derive methods to analyze user interest and compute recommendations. We evaluate the use of tag based recommendation on the Movielens dataset and a dataset of tagged books.