Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/164195
Title: Contextual Operation for Recommender Systems
Authors: Shu Wu;Qiang Liu;Liang Wang;Tieniu Tan
subject: context-aware|context representation|context operation|Recommender systems|contextual information
Year: 2016
Publisher: IEEE
Abstract: With the rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevances between contexts and users/items. However, such kind of relevance has much difficulty in explanation. Some works on multi-domain relation prediction can also be used for the context-aware recommendation, but they have limitations in generating recommendations under a large amount of contextual information. Motivated by recent works in natural language processing, we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item. Besides, we use the contextual operating tensor to capture the common semantic effects of contexts. Experimental results show that the proposed Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical datasets. From the experimental results of COT, we also obtain some interesting observations which follow our intuition.
Description: 
URI: http://localhost/handle/Hannan/164195
ISSN: 1041-4347
volume: 28
issue: 8
More Information: 2000
2012
Appears in Collections:2016

Files in This Item:
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Title: Contextual Operation for Recommender Systems
Authors: Shu Wu;Qiang Liu;Liang Wang;Tieniu Tan
subject: context-aware|context representation|context operation|Recommender systems|contextual information
Year: 2016
Publisher: IEEE
Abstract: With the rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevances between contexts and users/items. However, such kind of relevance has much difficulty in explanation. Some works on multi-domain relation prediction can also be used for the context-aware recommendation, but they have limitations in generating recommendations under a large amount of contextual information. Motivated by recent works in natural language processing, we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item. Besides, we use the contextual operating tensor to capture the common semantic effects of contexts. Experimental results show that the proposed Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical datasets. From the experimental results of COT, we also obtain some interesting observations which follow our intuition.
Description: 
URI: http://localhost/handle/Hannan/164195
ISSN: 1041-4347
volume: 28
issue: 8
More Information: 2000
2012
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7464889.pdf996.72 kBAdobe PDFThumbnail
Preview File
Title: Contextual Operation for Recommender Systems
Authors: Shu Wu;Qiang Liu;Liang Wang;Tieniu Tan
subject: context-aware|context representation|context operation|Recommender systems|contextual information
Year: 2016
Publisher: IEEE
Abstract: With the rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevances between contexts and users/items. However, such kind of relevance has much difficulty in explanation. Some works on multi-domain relation prediction can also be used for the context-aware recommendation, but they have limitations in generating recommendations under a large amount of contextual information. Motivated by recent works in natural language processing, we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item. Besides, we use the contextual operating tensor to capture the common semantic effects of contexts. Experimental results show that the proposed Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical datasets. From the experimental results of COT, we also obtain some interesting observations which follow our intuition.
Description: 
URI: http://localhost/handle/Hannan/164195
ISSN: 1041-4347
volume: 28
issue: 8
More Information: 2000
2012
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7464889.pdf996.72 kBAdobe PDFThumbnail
Preview File