Efficient Features for Movie Recommendation Systems

Sammanfattning: User written movie reviews carry substantial amounts of movie related features such as description of location, time period, genres, characters, etc. Using natural language processing and topic modeling based techniques, it is possible to extract features from movie reviews and find movies with similar features. In this thesis, a feature extraction method is presented and the use of the extracted features in finding similar movies is investigated. We do the text pre-processing on a collection of movie reviews. We then extract topics from the collection using topic modeling techniques and store the topic distribution for each movie. Similarity metrics such as Hellinger distance is then used to find movies with similar topic distribution. Furthermore, the extracted topics are used as an explanation during subjective evaluation. Experimental results show that our extracted topics represent useful movie features and that they can be used to find similar movies efficiently.