Users’ ratings in recommender systems can be predicted by their historical data, item content, or preferences. In recent literature, scientists have used complex networks to model a user–user or an item–item network of the RS. Also, community detection methods can cluster users or items to improve the prediction accuracy further. However, the number of links in modeling a network is too large to do proper clustering, and community clustering is an NP-hard problem with high computation complexity. Thus, we combine fuzzy link importance and K-core decomposition in complex network models to provide more accurate rating predictions while reducing the computational complexity. The experimental results show that the proposed method can improve the prediction accuracy by 4.64% to 5.71% on the MovieLens data set and avoid solving NP-hard problems in community detection compared with existing methods. Our research reveals that the links in a modeled network can be reasonably managed by defining fuzzy link importance, and that the K-core decomposition can provide a simple clustering method with relatively low computation complexity.

中文翻译：

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推荐系统中的 K 核分解提高了评分预测的准确性

用户在推荐系统中的评分可以通过他们的历史数据、项目内容或偏好来预测。在最近的文献中，科学家们使用复杂的网络来模拟 RS 的用户-用户或项目-项目网络。此外，社区检测方法可以对用户或物品进行聚类，以进一步提高预测准确性。然而，网络建模中的链接数量太大而无法进行适当的聚类，并且社区聚类是一个具有高计算复杂度的 NP-hard 问题。因此，我们在复杂网络模型中结合模糊链接重要性和 K 核分解，以提供更准确的评级预测，同时降低计算复杂度。实验结果表明，该方法可以将预测精度提高4.64%至 5.71%在 MovieLens 数据集上，与现有方法相比，避免解决社区检测中的 NP-hard 问题。我们的研究表明，通过定义模糊链接重要性可以合理地管理建模网络中的链接，并且 K 核分解可以提供一种计算复杂度相对较低的简单聚类方法。