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A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce
International Journal for Research in Applied Science and Engineering Technology
Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken anddoi:10.22214/ijraset.2021.38844 fatcat:heerfcvzdfeljkw3x2orim77wy