基于Web日志挖掘和相关性度量的电子商务推荐系统

基于Web日志挖掘的个性化推荐技术已在电子商务网站中广泛应用，针对现有推荐系统的准确性不高等问题，提出一种基于Web日志挖掘和相关性度量的个性化推荐系统. 首先，提取用户的访问日志，并对其进行预处理，以获得精简的结构化数据. 然后，对日志进行分析，提取出特征序列. 再后，根据特征的出现频率和页面停留时间，计算出页面与交易文本文档的相关性. 最终，利用夹角余弦公式计算出用户与页面的相关性，并以此形成推荐列表. 实验结果表明，该方案能够根据用户偏好精确的给出个性化推荐.

E-Commerce Recommender System Based on Web Log Mining and Correlation Measure

Nowadays, personalized recommender technology based on Web log mining has been widely used in the e-commerce website. For the issues that the existing recommender systems do not have high accuracy, a recommendation system for e-commerce based on web log mining and correlation measure is proposed. First, the user''s access log is extracted, and the data is preprocessed to obtain the structured data. Then, the log is analyzed to extract the characteristic sequence. After that, the correlation between the page and the transaction text documents is calculated according to the occurrence frequency of characteristics and the page dwell time. Finally, the angle cosine formula is used to calculate the correlation between the user and the page, and thus form a list of recommendations. Experimental results show that the proposed scheme can accurately give personalized recommendation according to the user''s preference.