Analyzing reviews for rating prediction and item recommendation

GAO Yi-fan; YU Wen-zhe; CHAO Ping-fu; ZHENG Zhi-ling; ZHANG Rong

doi:10.3969/j.issn.1000-5641.2015.03.010

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May 2015

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Article Navigation > Journal of East China Normal University (Natural Sciences) > 2015 > (3): 80-90

GAO Yi-fan, YU Wen-zhe, CHAO Ping-fu, ZHENG Zhi-ling, ZHANG Rong. Analyzing reviews for rating prediction and item recommendation[J]. Journal of East China Normal University (Natural Sciences), 2015, (3): 80-90. doi: 10.3969/j.issn.1000-5641.2015.03.010

Citation:

GAO Yi-fan, YU Wen-zhe, CHAO Ping-fu, ZHENG Zhi-ling, ZHANG Rong. Analyzing reviews for rating prediction and item recommendation[J]. Journal of East China Normal University (Natural Sciences), 2015, (3): 80-90. doi: 10.3969/j.issn.1000-5641.2015.03.010

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Analyzing reviews for rating prediction and item recommendation

doi: 10.3969/j.issn.1000-5641.2015.03.010

Received Date: 2014-12-15
Publish Date: 2015-05-25

Abstract

Abstract

Recommender systems are widely deployed in Web applications that need to predict the preferences of users to items. They are popular in helping users find movies, books, music, and products in general. In this work, we design a method for item recommendation based on a novel model that captures correlations between hidden aspects in reviews and numeric ratings. It is motivated by the observation that a users preference against an item is affected by different aspects discussed in reviews. Our method first explores topic modeling to discover hidden aspects from review text. Profiles are then created for users and items separately based on aspects discovered in their reviews. Finally, we utilize logistic regression to model the user item relationship and the rating is modeled as the similarity between user and item profiles. Experiments over real world reviews demonstrate the advantage of our proposal over state of the art solution.
- recommendationhidden aspectLDAregression modelreview analysis,

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References(1)

References

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