富信息环境下基于兴趣模式的推荐系统研究

项目名称： 富信息环境下基于兴趣模式的推荐系统研究

项目编号： No.71271044

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 袁华

作者单位： 电子科技大学

项目金额： 56万元

中文摘要： 推荐结果的兴趣性一直是电子商务个性化推荐系统的关注重点。这一问题在传统推荐系统中因为方法和计算资源的局限，目前并未得到很好的解决。近年来随着富信息技术的渗透发展,电子商务相关活动数据海量增长,又对个性化推荐系统增加了结果多样性和速度高效性要求,传统推荐技术正面临着多项挑战。因此，本课题拟对: 1、基于关联兴趣模式的推荐方法；2、兴趣度指标及兴趣模式挖掘；3、面向富信息融合的多维推荐方法等技术展开深入研究,期望建立起富信息环境下基于兴趣模式的推荐系统。本研究的优点：通过兴趣度指标可以发现用户真正有兴趣的模式并用于推荐，将提高推荐的精确性；其次，改进方法使得在海量数据中挖掘较低支持度的兴趣模式可行；最后，兴趣模式结合富信息融合的推荐方法可以提高推荐的多样性，同时可以避免推荐结果的马太效应和冷启动问题。另外，本课题还将基于兴趣模式推荐技术对系统可能面临的欺诈攻击进行研究，并实现一个推荐原型系统。

中文关键词： 数据挖掘；推荐系统；兴趣模式；协同过滤；富信息

英文摘要： The interestingness of recommended results is the most concern in personalization recommender systems research. However, it has not been well resolved in traditional recommender systems on account of the limitations of recommendation technologies and computation resources. Further, the traditional recommendation technologies have to face more serious challenges from the huge amounts of data, recommendation diversity and speed efficiency in a rich-information era. To address these challenges,this project will conduct an in-depth study for the interesting pattern based recommendation technology in rich information environment and the main contents include:(1)constructing a new interesting pattern based recommendation method;(2)implementing a new mining method to find rare but valuable interesting pattern form huge amounts of data;(3)combining the recommendation method with the information fusion technology to improve the performance of recommendation systems. The advantages of the interesting pattern based recommender systems are as follows:(1) The accuracy of the system is improved by using the user interested patterns in recommendation technology; (2) The mining process of interesting patterns with very low minimum-support is feasible while the new evaluation method is introduced; (3)The diversity of recommende

英文关键词： Data mining；Recommendation systems；Interesting pattern；Collaborative filtering；Rich information