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Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems

Recommendation systems have become extremely common in recent years due to
the ubiquity of information across various applications. Online entertainment (e.g.,
Netflix), E-commerce (e.g., Amazon, Ebay) and publishing services such as Google
News are all examples of services which use recommender systems. Recommendation systems are rapidly evolving in these years, but these methods have fallen short in coping with several emerging trends such as likes or votes on reviews. In this work we have proposed a new method based on collaborative filtering by considering other users' feedback on each review. To validate our approach we have used Yelp data set with more than 335,000 product and service category ratings and 70,817 real users. We present our results using comparative analysis with other well-known recommendation systems for particular categories of users and items. / Graduate / 0984 / 0800 / yheshmat@uvic.ca

Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5605
Date26 August 2014
CreatorsHeshmat Dehkordi, Yasamin
ContributorsGanti, Sudhakar, Thomo, Alex
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
RightsAvailable to the World Wide Web, http://creativecommons.org/licenses/by/2.5/ca/

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