| 基于Web日志和聚类的协同过滤推荐算法 |
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| 引用本文： | 张校慧,魏增辉. 基于Web日志和聚类的协同过滤推荐算法[J]. 计算机时代, 2011, 0(1): 4-6 |
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| 作者姓名： | 张校慧 魏增辉 |
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| 作者单位： | 黄河水利职业技术学院信息工程系,河南开封,475004 |
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| 摘 要： | 协同过滤推荐算法是目前应用最为成功的一种电子商务推荐方法,但协同过滤算法也存在数据稀疏性和缺乏个性化等问题,这些问题影响了推荐算法的效率和准确性.针对以上问题,提出了引入Web日志分析的方法,同时利用用户聚类等相关技术,不仅解决了数据稀疏的问题也提高了推荐的准确性.
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| 关 键 词： | 日志分析 用户聚类 协同过滤 电子商务 |
Collaborative Filtering Recommendation Algorithm Based an Web Logs and Clustering |
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| ZHANG Xiao-hui,WEI Zeng-hui. Collaborative Filtering Recommendation Algorithm Based an Web Logs and Clustering[J]. Computer Era, 2011, 0(1): 4-6 |
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| Authors: | ZHANG Xiao-hui WEI Zeng-hui |
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| Affiliation: | 1. Dept. of Information Engineering, Yellow River Conservancy Technical Institute, Kaifeng, Henan 475004, China; 2. Dept. of Information Engineering, Yellow River Conservancy Technical Institute) |
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| Abstract: | Collaborative filtering recommendation algorithm is the most successful e-commerce recommendation method in use now, but collaborative filtering algorithm also has some problems such as data sparseness and lack of individuation, these problems affect the efficiency and accuracy of recommendation algorithm. For the above problems, we propose the method of introducing Web log analysis, and utilize user clustering technology as well as related technologies, which not only solve the problem of data sparseness but also improve the accuracy of recommendation. |
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| Keywords: | log analysis user clustering collaborative filtering e-commerce |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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