The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
复制标题
DOI：
10.1145/3331184.3331244
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Yiyi Tao;Yiling Jia;Nan Wang;Hongning Wang
中科院分区：
文献类型：
--
作者：
Yiyi Tao;Yiling Jia;Nan Wang;Hongning Wang
潜在因素模型在个性化推荐方面取得了巨大的成功，但它们也很难解释。在这项工作中，我们整合了回归树来指导潜在因素模型的学习，并使用学习到的树结构来解释产生的潜在因素。具体来说，我们使用用户生成的评论分别在用户和项目上构建回归树，并将潜在概要文件关联到树上的每个节点以表示用户和项目。随着回归树的生长，潜在因素在树结构的正则化作用下逐渐细化。因此，我们能够通过查看回归树上每个因素的路径来跟踪潜在概要文件的创建，从而作为对最终建议的解释。在Amazon和Yelp两大评论集上进行的大量实验表明，我们的模型优于几种具有竞争力的基线算法。此外，我们广泛的用户研究也证实了我们模型生成的可解释推荐的实用价值。
Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regularization imposed by the tree structure. As a result, we are able to track the creation of latent profiles by looking into the path of each factor on regression trees, which thus serves as an explanation for the resulting recommendations. Extensive experiments on two large collections of Amazon and Yelp reviews demonstrate the advantage of our model over several competitive baseline algorithms. Besides, our extensive user study also confirms the practical value of explainable recommendations generated by our model.