TY - GEN
T1 - Comparative preferences induction methods for conversational recommenders
AU - Trabelsi, Walid
AU - Wilson, Nic
AU - Bridge, Derek
PY - 2013
Y1 - 2013
N2 - In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user's preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study.
AB - In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user's preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study.
UR - https://www.scopus.com/pages/publications/84890053720
U2 - 10.1007/978-3-642-41575-3_28
DO - 10.1007/978-3-642-41575-3_28
M3 - Conference proceeding
AN - SCOPUS:84890053720
SN - 9783642415746
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 363
EP - 374
BT - Algorithmic Decision Theory - Third International Conference, ADT 2013, Proceedings
PB - Springer Verlag
T2 - 3rd International Conference on Algorithmic Decision Theory, ADT 2013
Y2 - 13 November 2013 through 15 November 2013
ER -