A movie recommendation method based on users' positive and negative profiles

Abstract

In the traditional content-based recommendation method, we usually use the movies users watched before or rated to represent their profile. However, there are many movies that users have never seen or rated. For an unrated movie, there are two possibilities: maybe the user likes it or does not like it. In this paper, we first focus on how to identify users' preferences for movies by using a collaborative filtering algorithm to predict the users’ movie ratings. We can then create two movie lists for each user, where one is the movies the user likes (with higher predicting or true ratings), and the other is the movies the user does not like (with lower predicting or true ratings). Based on these two movie lists, we establish a user positive profile and a user negative profile. Therefore, our algorithm will recommend to users movies that are most similar to their positive profile and most different from their negative profile. Finally, our experiments show that our method can improve the MAE index of the traditional collaborative filtering method by 12.54%, the MAPE index by 17.68%, and the F1 index by 10.16%.