A taxonomy based similarity measure using inference process to reduce cold start issue in collaborative filtering

초록

Collaborative filtering (CF) is one of the most widespread methods for recommendation approach. It has been used efficiently to help users to find items that they should appreciate, by identifying users that can be characterized as “similar”, using a similarity measure based on items rating data. However, traditional CF approaches have shown limitation occasioned by problems such as sparsity and cold start. This situation can cause a user to stop using a system due to lack of accuracy in produced recommendations. This paper suggests another source of information, item taxonomies, which have become extremely common among recommender systems for products classification in diverse domains. We combine such knowledge with an inference process on the taxonomy hierarchy to propose a new similarity measure, which focuses on improving recommendations under cold-start conditions. Experiments made on the MovieLens dataset indicate that our method improves results when applied to cold start situations.