用因果图发现推荐系统中真正影响反馈的少数关键变量。
Causal Discovery in Recommender Systems: Example and Discussion
- 结合观测数据与先验知识构建因果图
- 仅少数变量显著影响反馈信号
- 挑战大模型堆叠变量的流行趋势
因果性在人工智能和机器学习领域受到越来越多关注。本文以推荐系统问题为例，展示如何利用因果图建模。我们通过结合开源数据集的观测数据与先验知识，开展因果发现任务，学习得到一个因果图。结果表明，仅有少数变量对分析的反馈信号产生实质性影响。这一发现与当前机器学习领域将越来越多变量引入大规模模型（如神经网络）的趋势形成鲜明对比。
原文摘要 · Abstract (English)
Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal discovery task to learn a causal graph by combining observational data from an open-source dataset with prior knowledge. The resulting causal graph shows that only a few variables effectively influence the analysed feedback signals. This contrasts with the recent trend in the machine learning community to include more and more variables in massive models, such as neural networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。