| 基于MI聚类的协同推荐算法 |
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| 引用本文： | 袁汉宁,周彤,韩言妮,陈媛媛.基于MI聚类的协同推荐算法[J].武汉大学学报(信息科学版),2015,40(2):253-257. |
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| 作者姓名： | 袁汉宁 周彤 韩言妮 陈媛媛 |
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| 作者单位： | 1北京理工大学软件学院北京 100081;2武汉大学国际软件学院湖北 武汉 430079;3中国科学院声学研究所高性能网络实验室北京 100190;4武汉理工大学经济学院湖北 武汉 430070 |
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| 基金项目： | 国家自然科学基金资助项目(61173061,61472039,61303252,71201120);中央高校基本科研业务费专项资金资助项目(2012-IV-053)~~ |
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| 摘 要： | 在个性化推荐系统中,项目的内容特征是影响推荐精度的重要因素。针对传统协同推荐不能有效考虑项目内容特征的问题,在考虑传统用户-项目评分信息的基础上,引入项目的内容特征属性,构建基于多示例(MI)的用户评分信息表达模型。根据多示例学习模式具有一定容错性的特点,设计了基于多示例聚类的协同推荐算法,通过多示例聚类计算用户的最近邻集合,根据最近邻集合对用户评分进行预测。实验结果表明,基于MI聚类的协同过滤推荐算法提高了预测评分的准确度,且有效缓解了数据稀疏性问题
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| 关 键 词： | 协同推荐 MI聚类 内容特征 |
| 收稿时间： | 2013-04-25 |
Collaborative Recommendation Algorithm Based on MI Clustering |
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| YUAN Hanning;ZHOU Tong;HAN Yanni;CHEN Yuanyuan.Collaborative Recommendation Algorithm Based on MI Clustering[J].Geomatics and Information Science of Wuhan University,2015,40(2):253-257. |
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| Authors: | YUAN Hanning;ZHOU Tong;HAN Yanni;CHEN Yuanyuan |
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| Institution: | 1International School of Software Beijing Institute of Technology Beijing 100081 China;2International School of Software Wuhan University Wuhan 430079 China;3High Performance Network Laboratory Institute of Acoustics Chinese Academy of Sciences Beijing 100190 China;4School of Economics Wuhan University of Technology Wuhan 430070 China |
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| Abstract: | In a personalized recommendation system, the context feature of item is an important factor affecting recommendation accuracy, but traditional collaborative recommendation algorithms cannot take context feature of item into account effectively. To solve this problem we constructed a user-item ratings information representation model with multiple instance learning(MIL) based on traditional user-item ratings information, considering the context features of an item.Exploiting a characteristic trait of MIL, its strong tolerance to fault，a collaborative recommendation algorithm based on MI clustering was designed which computes users’ nearest neighbors by MI clustering and predicates user ratings according to the neighbors.Experimental recults confirmed that collaborative recommendation algorithm based on MI clustering improved accuracy of predictions and alleviated the problem of sparse data effectively. |
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