【摘要】 近年来,各类视频应用上内容越来越丰富,页面上与当前用户无关的内容也越来越多。因此,市面上出现了多种不同的推荐算法来进行内容推荐。但是,不是每种推荐算法都能够解决所有的问题。基于个性化推荐系统的视频App,融合了多种推荐方法。首先为了解决推荐系统的冷启动问题,采用了基于统计学的推荐方式,同时,采用基于协同过滤的推荐算法,计算视频和用户间的隐藏特征,最后还有实时推荐模块,能够根据用户近期的行为对推荐内容进行调整。更多还原

【Abstract】 In recent years, the content of various video applications is becoming more and more abundant, and there are more and more content on the page that has nothing to do with the current users. Therefore, there are many different recommendation algorithms in the market for content recommendation. However, only use a recommendation algorithm can not solve all the problems.The video App based on personalized recommendation system integrates various recommendation methods. Firstly, in order to solve the cold start problem of the recommendation system, a recommendation method based on statistics is adopted. At the same time, a recommendation algorithm based on collaborative filtering is adopted to calculate the hidden features between video and users. Finally, there is a real-time recommendation module, which can adjust the recommended content according to the recent behavior of users.更多还原