TY - GEN
T1 - User preference learning with multiple information fusion for restaurant recommendation
AU - Fu, Yanjie
AU - Liu, Bin
AU - Ge, Yong
AU - Yao, Zijun
AU - Xiong, Hui
N1 - Publisher Copyright:
Copyright © SIAM.
PY - 2014
Y1 - 2014
N2 - If properly analyzed, the multi-aspect rating data could be a source of rich intelligence for providing personalized restaurant recommendations. Indeed, while recommender systems have been studied for various applications and many recommendation techniques have been developed for general or specific recommendation tasks, there are few studies for restaurant recommendation by addressing the unique challenges of the multiaspect restaurant reviews. As we know, traditional collaborative filtering methods are typically developed for single aspect ratings. However, multi-aspect ratings are often collected from the restaurant customers. These ratings can reflect multiple aspects of the service quality of the restaurant. Also, geographic factors play an important role in restaurant recommendation. To this end, in this paper, we develop a generative probabilistic model to exploit the multi-aspect ratings of restaurants for restaurant recommendation. Also, the geographic proximity is integrated into the probabilistic model to capture the geographic influence. Moreover, the profile information, which contains customer/restaurantindependent features and the shared features, is also integrated into the model. Finally, we conduct a comprehensive experimental study on a real-world data set. The experimental results clearly demonstrate the benefit of exploiting multi-aspect ratings and the improvement of the developed generative probabilistic model.
AB - If properly analyzed, the multi-aspect rating data could be a source of rich intelligence for providing personalized restaurant recommendations. Indeed, while recommender systems have been studied for various applications and many recommendation techniques have been developed for general or specific recommendation tasks, there are few studies for restaurant recommendation by addressing the unique challenges of the multiaspect restaurant reviews. As we know, traditional collaborative filtering methods are typically developed for single aspect ratings. However, multi-aspect ratings are often collected from the restaurant customers. These ratings can reflect multiple aspects of the service quality of the restaurant. Also, geographic factors play an important role in restaurant recommendation. To this end, in this paper, we develop a generative probabilistic model to exploit the multi-aspect ratings of restaurants for restaurant recommendation. Also, the geographic proximity is integrated into the probabilistic model to capture the geographic influence. Moreover, the profile information, which contains customer/restaurantindependent features and the shared features, is also integrated into the model. Finally, we conduct a comprehensive experimental study on a real-world data set. The experimental results clearly demonstrate the benefit of exploiting multi-aspect ratings and the improvement of the developed generative probabilistic model.
KW - Co-matrix factorization
KW - Multi-information fusion
KW - Restaurant recommendation
UR - http://www.scopus.com/inward/record.url?scp=84959905779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959905779&partnerID=8YFLogxK
U2 - 10.1137/1.9781611973440.54
DO - 10.1137/1.9781611973440.54
M3 - Conference contribution
AN - SCOPUS:84959905779
T3 - SIAM International Conference on Data Mining 2014, SDM 2014
SP - 470
EP - 478
BT - SIAM International Conference on Data Mining 2014, SDM 2014
A2 - Zaki, Mohammed J.
A2 - Banerjee, Arindam
A2 - Parthasarathy, Srinivasan
A2 - Ning-Tan, Pang
A2 - Obradovic, Zoran
A2 - Kamath, Chandrika
PB - Society for Industrial and Applied Mathematics Publications
T2 - 14th SIAM International Conference on Data Mining, SDM 2014
Y2 - 24 April 2014 through 26 April 2014
ER -