As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

中文翻译：

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 稀疏信任数据挖掘


随着推荐系统的不断发展，研究人员正在利用信任数据来提高推荐预测的准确性并帮助用户找到相关信息。然而，具有信任数据的大型推荐系统存在信任稀疏问题，导致等级膨胀并严重影响信任传播的可靠性。本文提出了稀疏信任数据挖掘的新颖研究，包括稀疏信任的新概念、稀疏信任模型和信任挖掘框架。为大型推荐系统中信任相关的研究奠定了基础。新的信任挖掘框架基于定制的规范化函数和新颖的传递八卦信任模型，可以发现大规模用户网络中实体之间的潜在信任信息并将其应用于推荐系统。我们对现实世界和合成数据集进行全面的性能评估。结果证实我们的框架挖掘了新的信任并有效改善了稀疏信任问题。