| dc.description.abstract | Recommendation System is widely used on Movie over-the-top platforms. The most widely recommended method is to collect the viewing information provided by multiple users, and use the ratings as the basis to compare with users in the same interests. Based on the ratings given by similar neighbors to the project, the similarity of preferences between users is calculated. In the end, predictive recommendations are made to items that the user has not yet rated, and more and better suggestions are provided to the user.
However, despite the big data score as the foundation, this kind of rating-based recommendation system can not observe the single user′s viewing preferences, even based on the group preferences with common experience, but no longer watched by individuals. Or ratings, as the main purpose of the recommendation, ignore the unique differences in film information.
This paper hopes to establish a multi-indicator theme analysis through the establishment of positive and negative user files, increase the unique characteristics of single movie, and incorporate the previously ignored users′ low ratings to complete more customized and accurate personalized recommendation system. | en_US |