On exploiting classification taxonomies in recommender systems
Massive taxonomies for product classification are currently gaining popularity among e-commerce systems for diverse domains. For instance, Amazon.com maintains an entire plethora of hand-crafted taxonomies classifying books, movies, apparel and various other types of consumer goods. We use such taxonomic background knowledge for the computation of personalized recommendations, exploiting relationships between super-concepts and sub-concepts during profile generation. Empirical analysis, both offline and online, demonstrates our proposal's superiority over existing approaches when user information is sparse and implicit ratings prevail. Besides addressing the sparsity issue, we use parts of our taxonomy-based recommender framework for balancing and diversifying personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm. We evaluate our method using book recommendation data, including offline analysis on 361,349 ratings and an online study involving more than 2,100 subjects.
taxonomies collaborative filtering topic diversification metrics