Recommendation based on contextual opinions

Guanliang Chen, Li CHEN

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

28 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 - 22nd International Conference, UMAP 2014, Proceedings
EditorsVania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, Geert-Jan Houben
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)9783319087856
Publication statusPublished - 2014
Event22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Aalborg, Netherlands
Duration: 7 Jul 201411 Jul 2014

Publication series

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


Conference22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

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


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