Exploiting crowd sourced reviews to explain movie recommendation Streaming services such as Netflix, MGo, and Hulu use advanced recommender systems to help their customers identify relevant content quickly and easily. These recommenders display the list of recommended movies organized in sublists labeled with the genre or some more specific labels. Unfortunately , existing methods to extract these labeled sublists require human annotators to manually label movies, which is timeconsuming and biased by the views of annotators. In this paper, we design a method that relies on crowd sourced reviews to automatically identify groups of similar movies and label these groups. Our method takes the content of movie reviews available online as input for an algorithm based on Latent Dirichlet Allocation (LDA) that identifies groups of similar movies. We separate the set of similar movies that share the same combination of genre in sublists and personalize the movies to show in each sublist using matrix factorization. The results of a sidebyside comparison of our method against Technicoloru0027s MGo VoD service are encouraging.

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis Traditional recommendation algorithms measure usersxe2x80x99 online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Usersxe2x80x99 reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the finegrained analysis of emotions expressed in usersxe2x80x99 online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts featuresentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user featuresentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy.