A contextual-bandit algorithm for mobile context-aware recommender system

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Abstract

Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user's content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user's situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages324-331
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 19 Nov 2012
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7665 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
Country/TerritoryQatar
CityDoha
Period12/11/1215/11/12

Keywords

  • Artificial intelligence
  • Exploration/exploitation dilemma
  • Machine learning
  • Recommender system

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