Recommendation based on contextual opinions

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24 Citations (Scopus)


Context has been recognized as an important factor in constructing personalized recommender systems. However, most contextaware recommendation techniques mainly aim at exploiting item-level contextual information for modeling users’ preferences, while few works attempt to detect more fine-grained aspect-level contextual preferences. Therefore, in this article, we propose a contextual recommendation algorithm based on user-generated reviews, from where users’ contextdependent preferences are inferred through different contextual weighting strategies. The context-dependent preferences are further combined with users’ context-independent preferences for performing recommendation. The empirical results on two real-life datasets demonstrate that our method is capable of capturing users’ contextual preferences and achieving better recommendation accuracy than the related works.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization
Subtitle of host publication22nd International Conference, UMAP 2014 Aalborg, Denmark, July 7-11, 2014 Proceedings
EditorsVania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben
Place of PublicationCham Switzerland
Number of pages13
ISBN (Electronic)9783319087863
ISBN (Print)9783319087856
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014 - Aalborg, Netherlands
Duration: 7 Jul 201411 Jul 2014
Conference number: 22nd (Conference Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on User Modelling, Adaptation, and Personalization (was AH and UM) 2014
Abbreviated titleUMAP 2014
Internet address


  • Aspect-level context
  • Context-aware recommender systems
  • Context-dependent preferences
  • Opinion mining
  • User-generated reviews

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