典型推荐算法在视频媒体中的应用研究
摘要
随着互联网规模的迅速增长,"信息超载"问题不断加剧,推荐系统应运而生。本文以两种经典的推荐算法——基于内容的推荐与协同过滤推荐方法为基本点向外扩散,系统阐述了推荐算法的核心模型与通用计算,并利用Movie Lens标准数据集,实现对电影系统的仿真推荐。最后,根据系统评价结果给出了两种算法的实用评价和前景。
With the rapid growth of Internet scale,the problem of information overload is getting worse and the recommendation system came into being.In this paper,we take two classical recommendation algorithms--content-based recommendation and collaborative filtering recommendation methods as the basic points to expand outwards,and systematically elaborate the core model and general calculation of the recommendation algorithm.Then,we make use of the standard data set of Movie Lens to implement the simulation recommendation for the Movie system.Finally,the practical evaluation and prospect of the two algorithms are given based on the system evaluation results.
With the rapid growth of Internet scale,the problem of information overload is getting worse and the recommendation system came into being.In this paper,we take two classical recommendation algorithms--content-based recommendation and collaborative filtering recommendation methods as the basic points to expand outwards,and systematically elaborate the core model and general calculation of the recommendation algorithm.Then,we make use of the standard data set of Movie Lens to implement the simulation recommendation for the Movie system.Finally,the practical evaluation and prospect of the two algorithms are given based on the system evaluation results.
引文
[1]胡新明.基于商品属性的电子商务推荐系统研究[D].华中科技大学,2012.
[2]唐积益.推荐系统中相似度计算方法的研究[D].江苏科技大学,2015.
[2]唐积益.推荐系统中相似度计算方法的研究[D].江苏科技大学,2015.