Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems

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dc.contributor.authorHeshmat Dehkordi, Yasamin
dc.contributor.supervisorGanti, Sudhakar
dc.contributor.supervisorThomo, Alex
dc.date.accessioned2014-08-26T17:28:11Z
dc.date.available2014-08-26T17:28:11Z
dc.date.copyright2014en_US
dc.date.issued2014-08-26
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractRecommendation 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.en_US
dc.description.proquestcode0984en_US
dc.description.proquestcode0800en_US
dc.description.proquestemailyheshmat@uvic.caen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/5605
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/ca/*
dc.subjectrecommender systemsen_US
dc.subjectcollaborative filteringen_US
dc.subjectperformance metricsen_US
dc.subjectYelp data seten_US
dc.titleIncorporating User Reviews as Implicit Feedback for Improving Recommender Systemsen_US
dc.typeThesisen_US

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