Similarity-Weighted Association Rules for a Name Recommender System
نویسنده
چکیده
Association rules are a simple yet powerful tool for making item-based recommendations. As part of the ECML PKDD 2013 Discovery 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 treatment of special cases, we produce one of the top-performing recommender systems in the discovery challenge.
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