基于用户兴趣变化融合的个性化推荐模型

摘要

移动互联网的发展带来了大量的应用,提供个性化服务和个性化推荐是解决用户“应用迷航”的有效手段,针对与某电信运营商“游戏”应用平台类似的应用商店领域,提出了一种融合的个性化推荐解决方案.该方案通过对用户行为日志的分析生成用户的兴趣偏好模型,同时引入时间因子反映用户兴趣的漂移,将基于用户偏好分析的推荐方法与基于物品的协同过滤算法相结合形成了融合的个性化推荐模型.实验对比结果表明,该模型避免了两算法之不足,发挥了两算法的优势,有效地提高了该应用平台的综合推荐性能.%With the development of mobile Internet,a large number of applications swarm into application stores.Personalized service and recommendation are effective methods to solve the problem —application-mazing.Aimed at some Telecom's IGame application platform and other similar application stores,a fused personalized recommendation solution is proposed.Through analyzing users' operation logs,the user's interest preference model is generated.At the same time,time factor which reflects the drift of users' interest is introduced.At last,the method based on user's preference analysis recommendation is combined with the item based collaborative filtering algorithm.The experimental results show that our model can avoid the shortage of above two algorithms and keep their advantages.The system' s comprehensive performance of personalized recommendation is improved effectively.