摘要
针对现有个性化商品推荐算法精度不高、新商品不能及时推荐等缺点,提出了一种基于商品特征、用户购买日志及用户实时浏览行为的个性化推荐算法。算法首先根据客户的在线浏览情况获取当前客户的购买倾向,然后将客户的购买日志与商品特征数据库进行对比分析,获得客户对商品特征的偏爱度及推荐参照组,依据特征实体的相似度矩阵进行特征推荐组推荐,最后结合当前的购买倾向向客户推荐商品。
In the field of personalized recommendation,current algorithms have the disadvantages of lower precision and deficiency on recommendation.This paper presents an algorithm based on the features of product,the customer＇s purchased logs and real-time browsing action.Firstly the content of on-line browsing is collected to deduce the purchase preference of current customer,then contrasts the purchased logs and the database of product features and analyzes them,by means of which,the preference degree of the product features and corresponding commendation reference groups can be obtained ,therefore according to the similarity matrix of features entity ,reference groups are recommended.Finally,integrated with the purchase preference and the former results,products are recommended to the customer.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第17期194-197,共4页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863) (the National High- Tech Research and Development Plan of China under Grant No.2002AA414060)
陕西省自然科学基金(the Natural Science Foundation of Shaanxi Province of China under Grant No.2005F05)
关键词
商品特征
个性化推荐
偏爱度
相似度
product features
personalized recommendation
preference degrees
similarity
