An dynamic-weighted collaborative filtering approach to address sparsity and adaptivity issues
An dynamic-weighted collaborative filtering approach to address sparsity and adaptivity issues
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DOI：
10.1109/cec.2014.6900403
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Liang Gu;Peng Yang;Yongqiang Dong
中科院分区：
文献类型：
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作者：
Liang Gu;Peng Yang;Yongqiang Dong
推荐系统作为解决信息过载和个性化服务问题的有效手段，受到了研究界的广泛关注。协同过滤是基于用户-项目矩阵的推荐系统中最成功的技术之一。通常，由于大量的用户和项目，矩阵是非常稀疏的。用户和项目的稀疏程度往往有显着差异。该矩阵的特征随用户/项目数据的变化而变化，从而导致推荐方法的可扩展性差。本文提出了一种动态加权协同过滤方法（WCF），以解决稀疏性和自适应性问题。在该方法中，考虑了相似用户和项目分布之间的关系，以获得更好的推荐，即，用户部分和项目部分对推荐结果的贡献取决于它们的相似性比率。此外，不同部分的影响强度由平均参数控制。在MovieLens数据集上的实验表明，在不同的数据稀疏性条件下，本文提出的DWCF方法都能获得较好的推荐效果，其性能优于基于用户的预测器、基于项目的预测器和传统的混合方法.
Recommendation systems, as efficient measures to handle the information overload and personalized service problems, have attracted considerable attention in research community. Collaborative filtering is one of the most successful techniques based on the user-item matrix in recommendation systems. Usually the matrix is extremely sparse due to the massive number of users and items. And the sparsity of users and items tends to differ significantly in degree. The feature of the matrix changes with the variation of users/items data and hence, leads to poor scalability of the recommendation method. This paper proposes a dynamic-weighted collaborative filtering approach (DWCF) to address sparsity and adaptivity issues. In this approach, the relationship between the distributions of similar users and items is considered to get better recommendation, i.e., the contributions of the user part and the item part to recommendation results depend on their similarity ratios. Moreover, the effect strength of different parts is controlled by an averaging parameter. Experiments on MovieLens dataset illustrate that the DWCF approach proposed in this paper can obtain good recommendation result given different conditions of data sparsity and perform better than a user-based predictor, an item-based predictor and a conventional hybrid approach.