Improve Recommendation Quality with Item Taxonomic Information

Abstract

Recommender systems’ performance can be easily affected when there are no sufficient item preferences data provided by previous users. This problem is commonly referred to as cold-start problem. This paper suggests another information source, item taxonomies, in addition to item preferences for assisting recommendation making. Item taxonomic information has been popularly applied in diverse ecommerce domains for product or content classification, and therefore can be easily obtained and adapted by recommender systems. In this paper, we investigate the implicit relations between users’ item preferences and taxonomic preferences, suggest and verify using information gain that users who share similar item preferences may also share similar taxonomic preferences. Under this assumption, a novel recommendation technique is proposed that combines the users’ item preferences and the additional taxonomic preferences together to make better quality recommendations as well as alleviate the cold-start problem.KeywordsRecommender SystemTaxonomyEcommerce