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Title:IMDB Review Mining and Movie Recommendation
Author(s):Guan, Yingjun
Subject(s):text mining
recommender system
data mining
Abstract:The world of movies contains enormous information and is worth digging for prediction and recommendation. IMDB database is one good example for extracting the relevant features, including both the item-based information (only related to the movies), for example, the title, the poster, the trailer, the genre, the cast, the director, etc., and the user-related information, such as name, gender, age, occupation, review, number of watching (the same movie), etc. Our project plans to benefit both the movie production corporations and the audience. For movie production corporations, our goals include better predicting the movie ratings given the item-based information and the user-related information. Data mining and supervised machine learning techniques are applied for labelling and classification to the nominal features. Text mining and sentimental analysis techniques are used for the review context in the database. For the audience, the hybrid recommendation system algorithm is introduced. The missing patterns of the features in the dataset are researched via visualizations and analyses. And special analysis on the “cold start” phenomenon (recommendation for new items or from insufficient information) on the database is also conducted.
Issue Date:2018-06-05
Publisher:Conference on Statistical Learning and Data Science / Nonparametric Statistics
Genre:Conference Poster
Date Available in IDEALS:2019-02-05

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