| 基于多目标优化双聚类的数字图书馆协同过滤推荐系统 |
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| 引用本文： | 刘飞飞.基于多目标优化双聚类的数字图书馆协同过滤推荐系统[J].图书情报工作,2011,55(7):111-113. |
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| 作者姓名： | 刘飞飞 |
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| 作者单位： | 中南林业科技大学图书馆 |
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| 摘 要： | 针对数字图书馆推荐系统，提出一种能够同时考虑用户和项之间的相似性的协同过滤(CF)方法，即应用多目标优化计算双聚类技术对行和列同时进行聚类，完成对用户和项相似性同时分组。为评估算法的效率，应用MovieLens数据集进行实验，结果表明该方法能够为用户提供有用的推荐意见，其性能优于其他CF方法。
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| 关 键 词： | 数字图书馆 推荐系统 个性化服务 协同过滤 多目标 双聚类 |
| 收稿时间： | 2010-08-18 |
Digital Library Collaborative Filtering Recommendation System Based on Multi-objective Evolutionary Biclustering |
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| Liu Feifei.Digital Library Collaborative Filtering Recommendation System Based on Multi-objective Evolutionary Biclustering[J].Library and Information Service,2011,55(7):111-113. |
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| Authors: | Liu Feifei |
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| Institution: | Library of Central South Forestry University, |
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| Abstract: | Personalized service technology has become the research focus of digital library.This paper proposes a multi-objective evolutionary biclustering technique to carry out clustering of rows and columns at the same time,and the algorithm is able to group similarities between users and items.In order to evaluate the proposed methodology,the paper applied it to MovieLens dataset which contains user's ratings to a large set of movies.The results indicate that the proposal is able to provide useful recommendations ... |
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| Keywords: | digital library recommendation system personalized service collaborative filtering multi-objective biclustering |
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