@inproceedings{774996c415cc492ea47fd04fd1beb191,
title = "Hybrid-ε-greedy for mobile context-aware recommender system",
abstract = "The wide development of mobile applications provides a considerable amount of data of all types. In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Our work consists in applying machine learning techniques and reasoning process in order to adapt dynamically the MCRS to the evolution of the user's interest. To achieve this goal, we propose to combine bandit algorithm and case-based reasoning in order to define a contextual recommendation process based on different context dimensions (social, temporal and location). This paper describes our ongoing work on the implementation of a MCRS based on a hybrid-ε-greedy algorithm. It also presents preliminary results by comparing the hybrid-ε-greedy and the standard ε-greedy algorithm.",
keywords = "Machine learning, contextual bandit, exploration/exploitation dilemma, personalization, recommender systems",
author = "Djallel Bouneffouf and Amel Bouzeghoub and Gan{\c c}arski, \{Alda Lopes\}",
year = "2012",
month = may,
day = "29",
doi = "10.1007/978-3-642-30217-6\_39",
language = "English",
isbn = "9783642302169",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "468--479",
booktitle = "Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings",
edition = "PART 1",
note = "16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 ; Conference date: 29-05-2012 Through 01-06-2012",
}