摘要
随着电子商务的迅速发展,推荐系统在这些网站中得到了广泛的应用。目前应用最广泛的个性化推荐算法是协同过滤推荐算法,但是该方法存在稀疏矩阵与冷启动问题。根据用户浏览记录推荐商品是缓解这些问题的一个重要研究方向,这些方法根据用户在电子商务网站的访问日志,提取出用户的浏览路径序列,即用户浏览轨迹,为用户推荐偏爱商品。目前,通过分析用户浏览路径为用户推荐商品的方法主要依据用户浏览轨迹模式匹配或者从用户浏览轨迹中商品与下一个商品关系的角度进行考虑。而本研究从浏览轨迹中被浏览商品与最终被购买商品关系的角度出发,并以此为基础建立用户浏览轨迹偏爱模型,挖掘用户偏爱,为用户推荐商品。实验表明,所提方法能够在一定程度上解决因为新用户缺少历史购买及评分记录而引起的新用户冷启动问题,提高了推荐方法的准确度与召回率。
With the rapid development of E-commerce, recommendation system has been widely used in the Websites. Currently the collaborative filtering recommendation algorithm is the most widely used, however, this kind of methods has sparse matrix and cold-start problems. In order to solve or at least improve these problems, methods based on users＇ browsing records were proposed. These methods extract every user＇ s browsing path sequence called user browsing tracks from the users＇ access log,and then recommend preference commodities for the user based on the analyzing re- sult of the tracks. By now, the most methods that recommend commodities for users through analysis browsing path are based on sequence pattern matching or the view of the relationship between commodity and the next browsed commodity. We considered from the view of the relationship between browsing commodities and eventually bought commodities, establishing the user browsing tracks preference model based on this, mining users＇ preference, and recommending products for new users. Experiments show that our method plays a certain role in solving the problem of cold-start for new users and enhancing the accuracy and recall rate of the recommendation system in E-commerce.
出处
《计算机科学》
CSCD
北大核心
2016年第12期223-228,共6页
Computer Science
基金
中央高校基本科研业务费(YS1404)资助
关键词
个性化推荐
浏览轨迹
冷启动
Personalized recommendation,Browsing tracks,Cold-start
