标题： TriMat：通过三矩阵分解的上下文感知推荐 显示英文标题

标题： TriMat: Context-aware Recommendation by Tri-Matrix Factorization

摘要： 搜索引擎是Web 2.0的象征性技术，许多人过去曾认为推荐系统是Web 3.0的新前沿。 在过去10年中，随着TikTok和类似应用的出现，推荐系统实现了机器学习先驱者的愿景。 然而，该领域的许多研究主题直到今天仍未解决。 其中一个主题是CARS（Context-aware Recommender Systems），这在很大程度上是一个理论性问题，而在实际应用中进展甚微。 在本文中，我们利用三矩阵分解技术将上下文信息纳入我们的矩阵分解框架，并证明我们的技术在实验中有效提高了准确性和公平性指标。 显示英文摘要

摘要： 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.