Zhang Zhuangzhuang, Dong Yunquan
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China.
Entropy (Basel). 2022 Apr 27;24(5):609. doi: 10.3390/e24050609.
In traditional recommendation algorithms, the users and/or the items with the same rating scores are equally treated. In real world, however, a user may prefer some items to other items and some users are more loyal to a certain item than other users. In this paper, therefore, we propose a measure by exploiting the difference in user-item relationships. In particular, we refer to the most important item of a user as his and the most important user of an item as its . We also propose a Core-User-Item Solver (CUIS) to calculate the core users and core items of the system, as well as the weighting coefficients for each user and each item. We prove that the CUIS algorithm converges to the optimal solution efficiently. Based on the weighted similarity measure and the obtained results by CUIS, we also propose three effective recommenders. Through experiments based on real-world data sets, we show that the proposed recommenders outperform corresponding traditional-similarity based recommenders, verify that the proposed weighted similarity can improve the accuracy of the similarity, and then improve the recommendation performance.
在传统推荐算法中，具有相同评分分数的用户和/或物品被同等对待。然而，在现实世界中，一个用户可能更喜欢某些物品而非其他物品，并且一些用户对某个物品比其他用户更忠诚。因此，在本文中，我们通过利用用户 - 物品关系中的差异提出一种度量方法。具体而言，我们将用户最重要的物品称为其“主物品”，将物品最重要的用户称为其“主用户”。我们还提出了一种核心用户 - 物品求解器（CUIS）来计算系统的核心用户和核心物品，以及每个用户和每个物品的加权系数。我们证明了CUIS算法能有效地收敛到最优解。基于加权相似性度量以及CUIS获得的结果，我们还提出了三种有效的推荐器。通过基于真实世界数据集的实验，我们表明所提出的推荐器优于相应的基于传统相似性的推荐器，验证了所提出的加权相似性可以提高相似性的准确性，进而提高推荐性能。
