Using Taxonomies for Product Recommendation

In this work we take advantage of valuable information encoded in taxonomies to improve the quality of recommender systems. We present three strategies that explore the use of taxonomies: (i) category descriptors, (ii) classification features and (iii) category filters. We provide a real-case study over the book domain, in which the recommendation target is a set of 100 news page from The New York Times and the items to be recommended are 1,499,792 books distributed in 1,621 category nodes from a taxonomy, both crawled from Amazon.com. In strategy (i), term descriptors of each category are combined with text descriptions of the books assigned to the category and terms that are representative of the category are added to the target page. In strategy (ii), categories that are strongly related to the target page are put together by a classifier that plays the role of a feature generator and these features are then used in the recommendation process. In strategy (iii), the output of the two strategies previously described are filtered so that only books from the same categories as the ones assigned to the target page are kept in it. We implement several methods that apply the three strategies individually and in combination. Experimental results indicate that our strategies can be successfully applied to improving traditional content-based recommender systems. In particular, when the target page is automatically assigned to a category, we obtain gains close to 13% in average precision. On the other hand, if such an assignment is made a priori, e.g., by the author or by a content editor, the gains are close to 20% in average precision.