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Title:Learning Semantic Representation from Restaurant Reviews: A Study of Yelp Dataset
Author(s):Zhao, Sanqiang; Han, Shuguang; Meng, Rui; He, Daqing; Zhang, Danchen
Subject(s):Semantic representation
Contextual information modeling
Recommender system
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.
Issue Date:2017
Citation Info:Zhao, S., Han, S., Meng, R., He, D. & Zhang, D. (2017). Learning Semantic Representation from Restaurant Reviews: A Study of Yelp Dataset. In iConference 2017 Proceedings, Vol. 2 (pp. 159-162).
Series/Report:iConference 2017 Proceedings Vol. 2
Genre:Conference Poster
Rights Information:Copyright 2017 is held by the authors.
Date Available in IDEALS:2017-12-05

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