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Learning Semantic Representation from Restaurant Reviews: A Study of Yelp Dataset

Zhao, Sanqiang and Han, Shuguang and Meng, Rui and He, Daqing and Zhang, Danchen (2016) Learning Semantic Representation from Restaurant Reviews: A Study of Yelp Dataset. In: IConference 2017, 22 March 2017 - 25 March 2017, Wuhan, China.

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Abstract

Users' preference such as rating only provides uni-dimension information, but reasons behind users' preference may be related to various aspects of an item, such as the types, certain attributes. By observing user-generated review always provides such rich information, we proposed an item representation based on review data. This approach supports semantic operation, which could potentially enables more recommendation scenarios. Our experiments further demonstrated that this approach gained much better performance than classical item representation methods.


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Details

Item Type: Conference or Workshop Item (Poster)
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhao, Sanqiangsaz31@pitt.eduSAZ31
Han, Shuguangshh69@pitt.eduSHH69
Meng, Rui
He, Daqingdah44@pitt.eduDAH44
Zhang, Danchendaz45@pitt.eduDAZ45
Date: September 2016
Date Type: Publication
Event Title: IConference 2017
Event Dates: 22 March 2017 - 25 March 2017
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 19 Jun 2017 14:26
Last Modified: 01 Nov 2017 14:01
URI: http://d-scholarship.pitt.edu/id/eprint/32397

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  • Learning Semantic Representation from Restaurant Reviews: A Study of Yelp Dataset. (deposited 19 Jun 2017 14:26) [Currently Displayed]

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