TY - JOUR
T1 - Towards user profile-based interfaces for exploration of large collections of items
AU - Becerra, Claudia
AU - Jimenez, Sergio
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
Copyright © 2013 for the individual papers by the papers' authors.
PY - 2013
Y1 - 2013
N2 - 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 visualization is proposed arising an accuracy vs. interpretability tradeoff between tag and keyword-based profiles. While keyword-based profiles produce more accurate recommendations, tag-based profiles seems to be more readable, meaningful and convenient for creating profile-based user interfaces.
AB - 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 visualization is proposed arising an accuracy vs. interpretability tradeoff between tag and keyword-based profiles. While keyword-based profiles produce more accurate recommendations, tag-based profiles seems to be more readable, meaningful and convenient for creating profile-based user interfaces.
KW - Collaborative filtering
KW - Collaborative tagging systems
KW - Recommender systems
KW - Social tagging
KW - User interfaces
UR - http://www.scopus.com/inward/record.url?scp=84924985241&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:84924985241
SN - 1613-0073
VL - 1050
SP - 9
EP - 16
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 3rd Workshop on Human Decision Making in Recommender Systems, Decisions@RecSys 2013 - In Conjunction with the 7th ACM Conference on Recommender Systems, RecSys 2013
Y2 - 12 October 2013
ER -