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中文摘要：
摘要：鉴于电子商务网站推荐系统的需要，将用户兴趣分为长期兴趣和短暂兴趣，并提出一种基于长期兴趣和短暂兴趣的用户偏好表示法．利用web服务器数据库的数据，采用无监督学习方法，对用户注册信息进行挖掘，提取出用户长期兴趣．基于向量映射，对web服务器日志上的用户使用记录数据和内容数据进行分析，提取用户短暂兴趣．通过用户反馈信息修正“粗糙”用户偏好文档，使得用户偏好文档更新得以实现．最后，应用了实证案例验证了该方法的合理性和有效性．
英文摘要：
Abstract： In view of the needs of E-commerce website for recommendation system, user interests are divided into the long-term interest and the short-term interest, furthermore, based on the long-term interest and the short-term interest, a way to describe users＇ preference is proposed. On the basis of the data from the web server database, users＇ registration information can be fully mined to abstract users＇ long-term interest by using unsupervised learning. Both the records data and content data on the server log are analyzed to abstract users＇ short-term interest by vector mapping. Moreover, the rough profile presenting users＇ preference can be modified by dealing with users＇ feedback, as a rescut, updating users＇ preference profile becomes possible. Case analysis illustrates to a certain extent this method is reasonable and feasible.
同期刊论文项目
同项目期刊论文
Sentiment Feature Selection from Chinese Online Reviews Based on Statistical Natural Language Proces
Sentiment Analysis of Chinese Online Reviews: A Comparison of Factors Influencing the Classification
Feature-Opinion Pair Identification of Product Reviews in Chinese: A Domain Ontology Modelling Metho
Pricing Strategy on B2C E-commerce from the Perspective of Mutual Influence of Price and Online Prod
Sentiment Classification of Chinese Online Reviews: Analyzing and Improving Supervised Machine Learn
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