Rerating the Movie Scores in Douban through Word Embedding

Abstract

The movie scores in the social networking service website such as IMDb, Totten Tomatoes and Douban are important references to evaluate the movies. Always, it will influence the box office directly. However, the public rating has strong bias depended on the types of movies, release time, and ages and background of the audiences. To fix the bias and give a movie a fair judgement is an important problem. In the paper, we focus on the movie scores on Douban, which is one of the most famous Chinese movie network community. We decompose the movie scores into two parts. One is the basis scores based on the basic properties of movies. The other is the extra scores which represent the excess value of the movies. We use the word-embedding technique to reduce the movies in a small dense subspace. Then, in the reduced subspace, we use the k-means method to offer the similar movies a basis scores.