Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/165269
Title: Coupled Topic Model for Collaborative Filtering With User-Generated Content
Authors: Shu Wu;Weiyu Guo;Song Xu;Yongzhen Huang;Liang Wang;Tieniu Tan
subject: Collaborative filtering (CF)|recommender systems (RS)|topic model|user-generated content (UGC)
Year: 2016
Publisher: IEEE
Abstract: The user-generated content (UGC) is a type of dyadic information that provides description of the interaction between users and items (such as rating, purchasing, etc.). Most conventional methods incorporate either a user profile or the item description, which cannot well utilize this kind of content information. Some other works jointly consider user ratings and reviews, but they are based on the factorization technique and have difficulty in providing explanations on generated recommendations. In this study, a coupled topic model (CoTM) for recommendation with UGC is developed. By combining UGC and ratings, the method discussed in this study captures both the content-based preferences and collaborative preferences and, thus, can explain both the user and item latent spaces using the topics discovered from the UGC. The learned topics in CoTM can also serve as proper explanations for the generated recommendations. Experimental results show that the proposed CoTM model yields significant improvements over the compared competitive methods on two typical datasets, that is, MovieLens-10M and Citation-network V1. The topics discovered by CoTM can be used not only to illustrate the topic distributions of users and items, but also to explain the generated user-item recommendations.
URI: http://localhost/handle/Hannan/165269
ISSN: 2168-2291
2168-2305
volume: 46
issue: 6
More Information: 908
920
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530846.pdf969.11 kBAdobe PDFThumbnail
Preview File
Title: Coupled Topic Model for Collaborative Filtering With User-Generated Content
Authors: Shu Wu;Weiyu Guo;Song Xu;Yongzhen Huang;Liang Wang;Tieniu Tan
subject: Collaborative filtering (CF)|recommender systems (RS)|topic model|user-generated content (UGC)
Year: 2016
Publisher: IEEE
Abstract: The user-generated content (UGC) is a type of dyadic information that provides description of the interaction between users and items (such as rating, purchasing, etc.). Most conventional methods incorporate either a user profile or the item description, which cannot well utilize this kind of content information. Some other works jointly consider user ratings and reviews, but they are based on the factorization technique and have difficulty in providing explanations on generated recommendations. In this study, a coupled topic model (CoTM) for recommendation with UGC is developed. By combining UGC and ratings, the method discussed in this study captures both the content-based preferences and collaborative preferences and, thus, can explain both the user and item latent spaces using the topics discovered from the UGC. The learned topics in CoTM can also serve as proper explanations for the generated recommendations. Experimental results show that the proposed CoTM model yields significant improvements over the compared competitive methods on two typical datasets, that is, MovieLens-10M and Citation-network V1. The topics discovered by CoTM can be used not only to illustrate the topic distributions of users and items, but also to explain the generated user-item recommendations.
URI: http://localhost/handle/Hannan/165269
ISSN: 2168-2291
2168-2305
volume: 46
issue: 6
More Information: 908
920
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530846.pdf969.11 kBAdobe PDFThumbnail
Preview File
Title: Coupled Topic Model for Collaborative Filtering With User-Generated Content
Authors: Shu Wu;Weiyu Guo;Song Xu;Yongzhen Huang;Liang Wang;Tieniu Tan
subject: Collaborative filtering (CF)|recommender systems (RS)|topic model|user-generated content (UGC)
Year: 2016
Publisher: IEEE
Abstract: The user-generated content (UGC) is a type of dyadic information that provides description of the interaction between users and items (such as rating, purchasing, etc.). Most conventional methods incorporate either a user profile or the item description, which cannot well utilize this kind of content information. Some other works jointly consider user ratings and reviews, but they are based on the factorization technique and have difficulty in providing explanations on generated recommendations. In this study, a coupled topic model (CoTM) for recommendation with UGC is developed. By combining UGC and ratings, the method discussed in this study captures both the content-based preferences and collaborative preferences and, thus, can explain both the user and item latent spaces using the topics discovered from the UGC. The learned topics in CoTM can also serve as proper explanations for the generated recommendations. Experimental results show that the proposed CoTM model yields significant improvements over the compared competitive methods on two typical datasets, that is, MovieLens-10M and Citation-network V1. The topics discovered by CoTM can be used not only to illustrate the topic distributions of users and items, but also to explain the generated user-item recommendations.
URI: http://localhost/handle/Hannan/165269
ISSN: 2168-2291
2168-2305
volume: 46
issue: 6
More Information: 908
920
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7530846.pdf969.11 kBAdobe PDFThumbnail
Preview File