基于多示例学习技术的Web目录页面链接推荐
Link Recommendation in Web Index Page Based on Multi-Instance Learning Techniques
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摘要: 在Web目录页面中，向用户推荐其感兴趣的链接有助于用户高效地访问网络资源.然而，用户往往不愿花费很多时间来标记训练样本，其提供的数据可能只能说明某个目录网页是否包含其感兴趣的内容，而不能明确标示出其感兴趣的具体链接.由于训练数据中缺乏对链接的标记，但预测时却需要找出用户感兴趣的链接，这就使得Web目录页面链接推荐问题相当困难.CkNN-ROI算法被提出用于解决该问题.实验表明，CkNN-ROI算法在解决这一困难的链接推荐问题上比其他一些算法更为有效.Abstract: In Web index page, recommending links of interest is beneficial for users to access Web resources efficiently. However, users won't spend a lot of time labeling samples and the data provided by them may just indicate whether or not a Web index page contains contents in which they are interested but give no information about which link really meets their interests. Therefore, the problem of link recommendation in Web index page is quite difficult since the training data lacks links' label while prediction for links of interest in a new Web index page is required. This problem is converted to a unique multi-instance learning problem and then solved by the proposed CkNN-ROI algorithm. Experiments show that this algorithm is more effective than other ones on solving this difficult link recommendation problem.
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Keywords:
- multi-instance learning /
- machine learning /
- data mining /
- Web mining /
- link recommendation /
- Web index page
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