Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems
Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems
复制标题
用于互惠推荐系统中协同过滤的潜在因素模型和聚合算子
DOI：
10.1145/3298689.3347026
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
I. Palomares
中科院分区：
文献类型：
--
作者：
James Neve;I. Palomares
在线约会平台帮助人们联系可能是彼此的好伴侣。在过去十年中，它们对社会产生了重大影响，例如，现在美国约有三分之一的新关系是在网上开始的。推荐系统广泛应用于在线平台，例如在线约会和招聘网站。这些推荐方法与传统的用户-项目方法（比如那些在电影和购物网站上运行的方法）有着根本的不同，因为它们必须共同考虑双方的利益。潜在因素模型在用户项目推荐领域已经取得了显著的成功，但是还没有在用户对用户领域进行研究。在这项研究中，我们提出了一种利用潜在因素模型进行互惠推荐的新方法。我们还对不同偏好聚合策略的使用进行了首次分析，从而证明用于组合用户偏好分数的聚合函数对推荐系统的结果有重大影响。我们的评估结果报告了相对于之前的最近邻和基于内容的互惠推荐方法的显著改进，并表明潜在因素模型可以有效地用于比以前最先进的互惠推荐系统更大的数据集。
Online dating platforms help to connect people who might potentially be a good match for each other. They have exerted a significant societal impact over the last decade, such that about one third of new relationships in the US are now started online, for instance. Recommender Systems are widely utilized in online platforms that connect people to people in e.g. online dating and recruitment sites. These recommender approaches are fundamentally different from traditional user-item approaches (such as those operating on movie and shopping sites), in that they must consider the interests of both parties jointly. Latent factor models have been notably successful in the area of user-item recommendation, however they have not been investigated within user-to-user domains as of yet. In this study, we present a novel method for reciprocal recommendation using latent factor models. We also provide a first analysis of the use of different preference aggregation strategies, thereby demonstrating that the aggregation function used to combine user preference scores has a significant impact on the outcome of the recommender system. Our evaluation results report significant improvements over previous nearest-neighbour and content-based methods for reciprocal recommendation, and show that the latent factor model can be used effectively on much larger datasets than previous state-of-the-art reciprocal recommender systems.