TY - GEN
T1 - Recommendation based on contextual opinions
AU - Chen, Guanliang
AU - Chen, Li
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Aspect-level context
KW - Context-aware recommender systems
KW - Context-dependent preferences
KW - Opinion mining
KW - User-generated reviews
UR - http://www.scopus.com/inward/record.url?scp=84927521797&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08786-3_6
DO - 10.1007/978-3-319-08786-3_6
M3 - Conference proceeding
AN - SCOPUS:84927521797
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 73
BT - User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings
A2 - Dimitrova, Vania
A2 - Kuflik, Tsvi
A2 - Chin, David
A2 - Ricci, Francesco
A2 - Dolog, Peter
A2 - Houben, Geert-Jan
PB - Springer Verlag
T2 - 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014
Y2 - 7 July 2014 through 11 July 2014
ER -