Opinionated Explanations of Recommendations from Product Reviews

Recommender systems are now mainstream and people are increasingly relying on them to make decisions in situations where there are too many options to choose from. Yet many recommender systems act like “black boxes”, providing little or no transparency into the rationale of their recommendation process [1]. Related research in the field of recommender systems has focused on developing and evaluating new algorithms that provide more accurate recommendations. However, the most accurate recommender systems may not necessarily be those that provide the most useful recommendations — due to the influence of how recommendations are presented and justified to users [2–4]. Therefore, recommender systems must be able to explain what they do and justify their actions in terms that are understandable to the user. An explanation, in this context, is any added information presented with recommendations to help users better understand why and how a recommendation is made [5]. Studies show that explanations help users make better decisions and are therefore provided for many reasons [6, 7], which normally align with the objective of the recommender system. Interestingly, explanations may sometimes be provided from the users (not from the recommender system) to justify their choices [8]. The availability of user-generated reviews that contain real experiences provides a new opportunity for recommender systems; yet, existing methods for explaining recommendations hardly take into account the implicit opinions that people express in such reviews even though studies show that users are increasingly relying on the reviews to make better choices [9]. Also, explanations usually provide a posthoc rationalisation for recommendations; but, this work is motivated by a more intimate connection between recommendations and explanations, which poses the question: can the recommendation process itself be guided