Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics.

Abstract

This short paper describes a novel technique for generating personalized tag recommendations for users of social bookmarking sites such as del.icio.us. Existing techniques recommend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.