Interactive recommending with Tag-Enhanced Matrix Factorization (TagMF)

Abstract

• TagMF , a recommendation method that enhances a latent factor model with additional information is presented. • The integrated model allows to provide richer interaction possibilities in Recommender Systems and to improve their transparency. • Users can express preferences through easily comprehensible tags instead of rating items, even in cold-start situations. • The method elucidates the hidden semantics of the factors and contributes to explaining a user’s preference profile. • Two user studies confirm that additional information improves, among others, perceived recommendation quality, as well as user experience in general. We introduce TagMF , a model-based Collaborative Filtering method that aims at increasing transparency and offering richer interaction possibilities in current Recommender Systems. Model-based Collaborative Filtering is currently the most popular method that predominantly uses Matrix Factorization: This technique achieves high accuracy in recommending interesting items to individual users by learning latent factors from implicit feedback or ratings the community of users provided for the items. However, the model learned and the resulting recommendations can neither be explained, nor can users be enabled to influence the recommendation process except by rating (more) items. In TagMF , we enhance a latent factor model with additional content information, specifically tags users provided for the items. The main contributions of our method are to use this integrated model to elucidate the hidden semantics of the latent factors and to let users interactively control recommendations by changing the influence of the factors through easily comprehensible tags: Users can express their interests, interactively manipulate results, and critique recommended items—at cold-start when no historical data is yet available for a new user, as well as in case a long-term profile representing the current user’s preferences already exists. To validate our method, we performed offline experiments and conducted two empirical user studies where we compared a recommender that employs TagMF against two established baselines, standard Matrix Factorization based on ratings, and a purely tag-based interactive approach. This user-centric evaluation confirmed that enhancing a model-based method with additional information positively affects perceived recommendation quality. Moreover, recommendations were considered more transparent and users were more satisfied with their final choice. Overall, learning an integrated model and implementing the interactive features that become possible as an extension to contemporary systems with TagMF appears beneficial for the subjective assessment of several system aspects, the level of control users are able to exert over the recommendation process, as well as user experience in general.