Explainable Recommendation through Attentive Multi-View Learning

By Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie. 2019

128 citations recorded by the source corpus.

Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e.g., Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e.g., node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “Gao et al. (2019) proposed an explainable deep multi-view learning framework to model multi-level features for explanation.”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “A few studies have focused on addressing this trade-off [84], however, such methods are not generalizable and applicable to all domains, in particular, task-speciﬁc ML/DL applications.”
When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “dation model, users are also interested in the comprehensible justifications for these recommendations [106], [107].”