Crowd-based personalized natural language explanations for recommendations

Explanations are important for users to make decisions on whether to take recommendations. However, algorithm gen- erated explanations can be overly simplistic and unconvinc- ing. We believe that humans can overcome these limita- tions. Inspired by how people explain word-of-mouth rec- ommendations, we designed a process, combining crowd- sourcing and computation, that generates personalized nat- ural language explanations. We modeled key topical as- pects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personal- ized the explanations presented to users based on their rat- ing history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag- based explanations, natural language explanations: 1) con- tain 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.