社交网络的软推荐系统（Social and Information Networks）

• 2020 年 1 月 16 日
• 筆記

最近的社交推荐系统利用朋友关系图来做出准确的推荐，因为社交网络中的朋友有着完全相同的兴趣和偏好。一些研究受益于硬聚类算法(如K-means)来确定用户之间的相似性，从而定义友谊的程度。在这篇论文中，我们更进一步，找到了真正的朋友，提出了更现实的建议。我们计算了用户之间的相似度，以及用户和项目之间的依赖度。我们的假设是，由于用户偏好的不确定性，模糊聚类，而不是经典的硬聚类，有利于准确的建议。我们加入了C-means算法来获得软用户集群的不同隶属度。然后，根据软聚类定义用户的相似度度量。然后，在一个训练方案中，我们确定了用户和项目的潜在代表，利用矩阵分解从庞大而稀疏的用户-项目-标签矩阵中提取。在参数调整中，我们找到了软社会正规化和用户-物品依赖项影响的最优系数。实验结果表明，与基于硬聚类的基线社会推荐系统相比，提出的模糊相似度度量方法提高了真实数据中的推荐效果。

原文题目：A Soft Recommender System for Social Networks

原文：Recent social recommender systems benefit from friendship graph to make an accurate recommendation, be- lieving that friends in a social network have exactly the same interests and preferences. Some studies have benefited from hard clustering algorithms (such as K-means) to determine the similarity between users and consequently to define degree of friendships. In this paper, we went a step further to identify true friends for making even more realistic recommendations. we calculated the similarity between users, as well as the depen- dency between a user and an item. Our hypothesis is that due to the uncertainties in user preferences, the fuzzy clustering, instead of the classical hard clustering, is beneficial in accurate recommendations. We incorporated the C-means algorithm to get different membership degrees of soft users’ clusters. Then, the users’ similarity metric is defined according to the soft clusters. Later, in a training scheme we determined the latent representations of users and items, extracting from the huge and sparse user-item-tag matrix using matrix factorization. In the parameter tuning, we found the optimum coefficients for the influence of our soft social regularization and the user-item dependency terms. Our experimental results convinced that the proposed fuzzy similarity metric improves the recommendations in real data compared to the baseline social recommender system with the hard clustering.

原文作者：Marzieh Pourhojjati-Sabet, Azam Rabiee

原文链接：https://arxiv.org/abs/2001.02520