Hybrid Filtering Learning to Rank Recommendation System based on user reviews

• 1、 Computer Science and Technology School,Beijing University of Posts and Telecommunications, Beijing, 100876

Abstract：Today in the big data environment, the recommended system to play an increasingly important role. How to find out from the dazzling array of products that users like is the most important task in the current recommended scenarios. In this paper, we propose a hybrid filtering hunger ranking learning recommendation system based on user comments. The user\'s comment on the movie is added as a feature to the recommendation algorithm. At the same time, a recommendation strategy based on content and collaborative filtering is proposed, and a heat calculation method is proposed. At the same time, the ranking learning algorithm BPR into the recommended algorithm, making the final recommendatiHybrid Filtering Learning to Rank Recommendation System based on user reviewson list performed better. Movie recommendation system needs high real-time and effectiveness. Database-level movie attributes and related data storage, user attributes and behavior data storage. Considering that the system is an online real-time system, the architecture of the whole system is divided into two parts, online and offline calculation. Offline calculation of learning model is conducive to the improvement of system efficiency. The model processing part mainly processes the data according to the recommended method to get the model.

Keywords： Artificial Intelligence Recommendation System Hybrid Filtering Topic Model Heat Calculation