Learning User Models in Multi-criteria Recommender Systems

Whenever people have to choose seeing or buying an item among many others, they are based on their own ways of evaluating its characteristics (criteria) to understand better which one of the items meets their needs. Based on this argument, in this paper we develop personalized models for each user, according to their ratings on specific criteria, and we use them in multi-criteria recommender systems. We assume the overall ranking, which indicates users’ final decision, is closely related to their given value in each criterion separately. We compare user models created using neural networks and linear regression and we show, as expected from the implicit nonlinear combination of criteria, that neural networks based models achieve better performance. In continue we investigate several different approaches of collaborative filtering and matrix factorization to make recommendations. For this purpose we estimate users’ similarity by comparing their models. Experimental justification is obtained using the Yahoo! Movie dataset.