Crowd-Based Personalized Natural Language Explanations for Recommendations

Explanations are ittiportant for users to notice decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowd sourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personal] the explanations presented to users based on their rating history. We evaluated the explanations by surveyilig 220 MovieLens users, finding that compared to personalized tag based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content -based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.