Similarity-Weighted Association Rules for a Name Recommender System
Benjamin Letham · 2013
Abstract. Association rules are a simple yet powerful tool for making item-based recommendations. As part of the ECML PKDD 2013 Dis-covery Challenge, we use association rules to form a name recommender system. We introduce a new measure of association rule confidence that incorporates user similarities, and show that this increases prediction performance. With no special feature engineering and no separate treat-ment of special cases, we produce one of the top-performing recommender systems in the discovery challenge.