跨领域建议:利用语义对齐和用户聚类来解决数据稀疏问题

Bahareh Rahmatikargar, Abdul Rafey Khan, Pooya Moraidan Zadeh, Ziad Kobti
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引用次数: 0

摘要

跨域推荐系统可以通过利用来自数据丰富领域的信息来改进数据稀疏领域的推荐来解决数据稀疏问题。在本研究中,我们考虑两个不同的域,它们共享共同的成员,但有不同的项目。本文提出了一种利用语义对齐和聚类技术来提高稀疏域推荐精度的新方法。我们首先使用域之间共享的语义信息来对齐域。在建立这种语义一致性之后,我们应用聚类技术对每个域中的相似用户进行分组。这些用户集群然后跨领域对齐,允许我们将知识从丰富领域的集群转移到稀疏领域。通过有效地弥合领域之间的差距,我们的方法可以提高推荐的准确性。我们已经在Amazon Movies和Amazon Books数据集上评估了我们提出的方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-Domain Recommendation: Leveraging Semantic Alignment and User Clustering to Address Data Sparsity
Cross-domain recommender systems can address data sparsity by leveraging information from a data-rich domain to improve recommendations in a data-sparse domain. In this study, we consider two distinct domains that share common members but have different items. We propose a new approach to enhance recommendation accuracy in the sparse domain by utilizing semantic alignments and clustering techniques. We begin the process by aligning the domains using shared semantic information between them. After establishing this semantic alignment, we apply clustering techniques to group similar users within each domain. These user clusters are then aligned across domains, allowing us to transfer knowledge from the richer domain’s clusters to the sparser domain. By effectively bridging the gap between the domains, our method can enhance the accuracy of the recommendation. We have evaluated the performance of our proposed approach on the Amazon Movies and Amazon Books datasets.
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