通过突出显示ExTrA构造的专家来提高推荐多样性
Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts
摘要：
如今，推荐系统（RSes）对个人用户和企业营销变得越来越重要，特别是在在线电子商务场景中。然而，虽然在文献中提出的大多数推荐算法都集中在提高预测精度的努力，推荐质量的其他重要方面，如多样性的建议，已或多或少被忽视。近十年来，推荐多样性引起了越来越多的研究关注，特别是在基于用户-项目二分网络的推荐模型中。在本文中，我们介绍了一个家庭的方法来提取制造的专家从用户的RSes，命名为专家跟踪方法（ExTrA的简称），并探讨这些制造的专家在提高推荐多样性的能力，突出他们在一个著名的二分网络为基础的方法，称为质量扩散（MD的简称）模型。这些ExTrA为基础的模型进行了比较，两个国家的最先进的MD改进模型HHP和BHC，在推荐的准确性和多样性。在三个真实世界数据集MovieLens，Netflix和RYM上的综合实证结果表明，我们提出的基于ExTrA的模型可以实现显着的多样性增益，同时保持相当的推荐准确性水平。
英文摘要：
Nowadays, recommender systems (RSes) are becoming increasingly important to individual users and business marketing, especially in the online e-commerce scenarios. However, while the majority of recommendation algorithms proposed in the literature have focused their efforts on improving prediction accuracy, other important aspects of recommendation quality, such as diversity of recommendations, have been more or less overlooked. In the latest decade, recommendation diversity has drawn more research attention, especially in the models based on user-item bipartite networks. In this paper, we introduce a family of approaches to extract fabricated experts from users in RSes, named as the Expert Tracking Approaches (ExTrA for short), and explore the capability of these fabricated experts in improving the recommendation diversity, by highlighting them in a well-known bipartite network-based method, called the Mass Diffusion (MD for short) model. These ExTrA-based models are compared with two state-of-the-art MD-improved models HHP and BHC, with respect to recommendation accuracy and diversity. Comprehensive empirical results on three real-world datasets MovieLens, Netflix and RYM show that, our proposed ExTrA-based models can achieve significant diversity gain while maintain comparable level of recommendation accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
