如何通过向因式分解机提供知识图谱使潜在因子可解释
How to make latent factors interpretable by feeding Factorization machines with knowledge graphs
摘要：
基于模型的推荐方法可以以非常高的准确度推荐项目。不幸的是，即使模型嵌入了基于内容的信息，如果我们移动到潜在空间，我们也会错过对推荐项的实际语义的引用。因此，这使得对推荐过程的解释变得重要。在本文中，我们展示了如何初始化潜在的因素，在因式分解机使用来自知识图谱的语义特征，以训练一个可解释的模型。通过我们的模型，语义特征被注入到学习过程中，以保留数据集中可用项目的原始信息量。使用两个著名的推荐系统数据集的训练模型的准确性和有效性进行了测试。通过依赖于原始知识图谱中编码的信息，我们还评估了最终模型的知识感知可解释性的语义准确性和鲁棒性。
英文摘要：
Model-based approaches to recommendation can recommend items with a very high level of accuracy. Unfortunately, even when the model embeds content-based information, if we move to a latent space we miss references to the actual semantics of recommended items. Consequently, this makes non-trivial the interpretation of a recommendation process. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model. With our model, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. The accuracy and effectiveness of the trained model have been tested using two well-known recommender systems datasets. By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
