Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular. However, the tangibility and trustworthiness of the recommendations are questionable due to the complexity and lack of explainability of the models. To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result. In this work, we present a general framework for both DNN and non-DNN models so that the counterfactual explainers all belong to it with specific choices of components. This framework first estimates the influence of a certain historical action after its removal and then uses search algorithms to find the minimal set of such actions for the counterfactual explanation. With this framework, we are able to investigate the relationship between the explainers and recommenders. We empirically study two recommender models (NCF and Factorization Machine) and two datasets (MovieLens and Yelp). We analyze the relationship between the performance of the recommender and the quality of the explainer. We observe that with standard evaluation metrics, the explainers deliver worse performance when the recommendations are more accurate. This indicates that having good explanations to correct predictions is harder than having them to wrong predictions. The community needs more fine-grained evaluation metrics to measure the quality of counterfactual explanations to recommender systems.
翻译:建议系统采用机器学习模型,从历史数据中学习历史数据,以预测用户的偏好。深神经网络(DNNN)模型,如神经合作过滤(NCF)模型越来越受欢迎。然而,由于这些模型的复杂性和解释性不足,建议系统的清晰度和可信度令人怀疑。为了便于解释,ACCENT和FIA等最新技术正在寻找反事实解释,这些技术是用户的具体历史行动,消除这些技术导致改变建议结果。在这项工作中,我们为DNN和非DNN模型提供了一个总框架,以便反事实解释者都属于它,并有具体的构成部分选择。这个框架首先估计了某些历史行动在被清除后的影响,然后使用搜索算法来找到这类行动最起码的一套反事实解释。有了这个框架,我们可以调查解释者和建议者之间的关系。我们从经验上研究两个建议模型(NCF和Cricalization Mach)和两个数据集(MoviLens和Ylp),我们分析了反事实解释者在准确性评估中是否更精确地解释了质量,我们用更精确的尺度来解释,我们用更精确的尺度来解释。我们用更精确的尺度来解释,我们用更精确的尺度来解释。我们用更精确的尺度来解释的尺度来解释的尺度来解释。我们用更精确的尺度来解释。我们用更精确的尺度来解释。我们用更精确的尺度来解释。我们用来来解释的尺度来解释。我们用更精确的尺度来解释。我们用来来解释。