| Resumen |
Collaborative tagging systems allow users to describe and organize items using labels in a free-shared vocabulary (tags), improving their browsing experience in large collections of items.
At present, the most accurate collaborative filtering techniques build user profiles in latent factor spaces that are not interpretable by users. In this paper, we propose a general method to build linear-interpretable user profiles that can be used for user interaction in a recommender system, using the well-known simple additive weighting model (SAW) for multi-attribute decision making. In experiments, two kinds of user profiles where
tested: one from free contributed tags and other from keywords
automatically extracted from textual item descriptions. We compare them for their ability to predict ratings and their potential for user interaction. As a test bed, we used a subset of the database of the University of Minnesota’s movie review system— Movielens, the social tags proposed by Vig et al. (2012) in their work “The Tag Genome”, and movie synopses extracted from the Netflix’s API. We found that, in “warm” scenarios, the proposed tag and keyword-based user profiles produce equal or better recommendations that those based on latent-factors obtained using matrix factorization. Particularly, the keyword-based approach obtained 5.63% of improvement. In cold-start conditions—movies without rating information, both approaches perform close to average. Moreover, a user profile visualizatio |
