TY - GEN
T1 - Exploration / exploitation trade-off in mobile context-aware recommender systems
AU - Bouneffouf, Djallel
AU - Bouzeghoub, Amel
AU - Gançarski, Alda Lopes
PY - 2012/12/26
Y1 - 2012/12/26
N2 - The contextual bandit problem has been studied in the recommender system community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles this problem by modeling the Mobile Context-Aware Recommender Systems (MCRS) as a contextual bandit algorithm and it is based on dynamic exploration/exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demonstrate that our algorithm outperforms surveyed algorithms.
AB - The contextual bandit problem has been studied in the recommender system community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles this problem by modeling the Mobile Context-Aware Recommender Systems (MCRS) as a contextual bandit algorithm and it is based on dynamic exploration/exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demonstrate that our algorithm outperforms surveyed algorithms.
KW - artificial intelligence
KW - exploration/exploitation dilemma
KW - machine learning
KW - recommender system
U2 - 10.1007/978-3-642-35101-3_50
DO - 10.1007/978-3-642-35101-3_50
M3 - Conference contribution
AN - SCOPUS:84871391272
SN - 9783642351006
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 591
EP - 601
BT - AI 2012
T2 - 25th Australasian Joint Conference on Artificial Intelligence, AI 2012
Y2 - 4 December 2012 through 7 December 2012
ER -