基于IPTV用户行为数据的个性化推荐系统的设计与实现

Design and Realization of Personalized Recommendation System Based on IPTV User Behavior Data

本文主要研究个性化推荐系统在新媒体行业的应用,通过对数据类型分类,设计联系用户和物品的评测模型,再利用数据采集架构采集用户行为数据,并用协同过滤算法得出推荐结果,解决了在IPTV领域用户量大、资源相对变化慢的个性化推荐问题;针对个性化推荐系统存在的问题,研究行业内相关成果,对个性化推荐系统的发展做出了展望,并对接下来的工作做了安排和计划。

This paper mainly studies the application of the personalized recommendation system in the new media industry： design the model between users and items through the data type classification; then collect the user behavior data by acquisition architecture, and get the recommended results using collaborative filtering algorithm. This way has solved the problems such as the personalized recommendation problem in the IPTV because of large number of users and resources which are relatively slow to change; Finally, analyze the related research in recommended areas in the light of the existing problems, meanwhile, discuss the development of personalized recommendation system and the next arrangements and plans.