Crowd-Based Personalized Natural Language Explanations for Recommendations

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Explainable Recommendation: A Survey and New Perspectives (2018, background): “Inspired by how people explain word-of-mouth recommendations, Chang et al. [2016] proposed a process that combines crowdsourcing and computation to generate personalized natural language explanations, and the authors also evaluated the generated natural language explanations in terms of efficiency, ”
User Preferences for Hybrid Explanations (2017, background): “To better understand user preferences, research has compared explanation styles [2], combined di erent explanation styles [24], and considered dimensions such as personalization [25, 26], tags [30], ranking [17], and natural language presentations [5].”
Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “For the ease of presentation, all our examples are from the domain of our evaluation dataset, movies, as this has been a fertile area of recommendation systems research both from an algorithmic and from an explainability perspective [6, 8, 17, 22, 48, 51].”