用三矩阵分解融合上下文信息，提升推荐系统准确率与公平性
TriMat: Context-aware Recommendation by Tri-Matrix Factorization
- 提出三矩阵分解框架，融合用户、物品与上下文三重信息
- 实验显示准确率与公平性指标均显著提升
- 适合关注推荐系统公平性与真实场景应用的研究者
搜索引擎是网络2.0的标志性技术，许多人曾认为推荐系统是网络3.0的新前沿。过去十年，随着TikTok等应用的兴起，推荐系统实现了机器学习先驱们的愿景。然而，该领域仍存在诸多未解决的问题，其中一项关键挑战是上下文感知推荐系统（CARS），尽管理论研究丰富，但实际应用进展有限。本文提出一种基于三矩阵分解的方法，将上下文信息融入矩阵分解框架，并在实验中证明该方法能有效提升推荐系统的准确率与公平性指标。
原文摘要 · Abstract (English)
Search engine is the symbolic technology of Web 2.0, and many people used to believe recommender systems is the new frontier of Web 3.0. In the past 10 years, with the advent of TikTok and similar apps, recommender systems has materialized the vision of the machine learning pioneers. However, many research topics of the field remain unfixed until today. One such topic is CARS (Context-aware Recommender Systems) , which is largely a theoretical topic without much advance in real-world applications. In this paper, we utilize tri-matrix factorization technique to incorporate contextual information into our matrix factorization framework, and prove that our technique is effective in improving both the accuracy and fairness metrics in our experiments.
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