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首页> 外文会议>E-Commerce and Web Technologies; Lecture Notes in Computer Science; 4082 >Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation
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Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation

机译：基于项目的协同过滤推荐的增强预测算法

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摘要

As the Internet infrastructure has been developed, a substantial number of diverse effective applications have attempted to achieve the full potential offered by the infrastructure. Collaborative Filtering recommender system, one of the most representative systems for personalized recommendations in E-commerce on the Web, is a system assisting users in easily finding the useful information. But traditional collaborative filtering suffers some weaknesses with quality evaluation: the sparsity of the data, scalability, unreliable users. To address these issues, we have presented a novel approach to provide the enhanced prediction quality supporting the protection against the influence of malicious ratings, or unreliable users. In addition, an item-based approach is employed to overcome the sparsity and scalability problems. The proposed method combines the item confidence and item similarity, collectively called item trust using this value for online predictions. The experimental evaluation on MovieLens data-sets shows that the proposed method brings significant advantages both in terms of improving the prediction quality and in dealing with malicious datasets.
机译：随着Internet基础结构的发展，大量多样的有效应用程序已尝试实现基础结构所提供的全部潜力。协作过滤推荐器系统是用于在Web上进行电子商务个性化推荐的最具代表性的系统之一，它是一个帮助用户轻松找到有用信息的系统。但是传统的协作过滤在质量评估方面存在一些弱点：数据的稀疏性，可伸缩性，不可靠的用户。为了解决这些问题，我们提出了一种新颖的方法来提供增强的预测质量，从而支持防止恶意评级或不可靠用户的影响。另外，采用基于项目的方法来克服稀疏性和可伸缩性问题。所提出的方法将项目置信度和项目相似性结合在一起，使用此值进行在线预测统称为项目信任。对MovieLens数据集的实验评估表明，该方法在提高预测质量和处理恶意数据集方面均具有显着优势。

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