Recommender systems help users to find information that fits their preferences in an overloaded search space. Collaborative filtering systems suffer from increasingly severe data sparsity problem because more and more products are sold in commercial websites, which largely constrains the performance of recommendation algorithms. User clustering has already been applied to recommendation on sparse data in the literature, but in a completely different way. In most existing works, user clustering is directly used to identify the similar users of the target user to whom we want to make recommendation. More specifically, the users who are clustered in the same group of the target user are considered as similar users. However, in this paper we use user clustering to reconstruct the user-item bipartite network such that the network density is significantly improved. The recommendation made on this dense network thus can achieve much higher accuracy than on the original sparse network. The experimental results on three benchmark data sets demonstrate that, when facing the problem of data sparsity, our proposed recommendation algorithm based on node clustering achieves a significant improvement in accuracy and coverage of recommendation.

推荐系统可帮助用户在过载的搜索空间中找到适合自己偏好的信息。协作过滤系统遭受越来越严重的数据稀疏性问题，这是因为越来越多的商品在商业网站上出售，这在很大程度上限制了推荐算法的性能。在文献中，用户聚类已经被用于推荐稀疏数据，但是方式却完全不同。在大多数现有作品中，用户聚类直接用于识别我们要向其推荐的目标用户的相似用户。更具体地说，将聚集在目标用户的同一组中的用户视为相似用户。然而，在本文中，我们使用用户聚类来重建用户项双向网络，从而显着提高网络密度。因此，与原始稀疏网络相比，在该密集网络上提出的建议可以实现更高的精度。在三个基准数据集上的实验结果表明，当面对数据稀疏性问题时，我们提出的基于节点聚类的推荐算法可以显着提高推荐的准确性和覆盖范围。