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- W2109489021 abstract "User written movie reviews carry substantial amounts of movie relatedfeatures such as description of location, time period, genres, characters,etc. Using natural language processing and topic modeling basedtechniques, it is possible to extract features from movie reviews and findmovies with similar features. In this thesis, a feature extraction methodis presented and the use of the extracted features in finding similarmovies is investigated. We do the text pre-processing on a collection ofmovie reviews. We then extract topics from the collection using topicmodeling techniques and store the topic distribution for each movie.Similarity metrics such as Hellinger distance is then used to find movieswith similar topic distribution. Furthermore, the extracted topics areused as an explanation during subjective evaluation. Experimental resultsshow that our extracted topics represent useful movie features andthat they can be used to find similar movies efficiently." @default.
- W2109489021 created "2016-06-24" @default.
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- W2109489021 date "2014-01-01" @default.
- W2109489021 modified "2023-09-26" @default.
- W2109489021 title "Efficient Features for Movie Recommendation Systems" @default.
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