Persuasive explanations for recommender systems: how explanations can influence users’ choices?

Recommender systems increasingly depend on explanations to convey the importance and relevance of the options recommended to their users. Previous research on explanations has predominantly concentrated on developing new explanation methods and studying how explanations can enhance recommendation effectiveness and understandability. However, limited attention has been given to understanding the persuasiveness of recommendations, particularly whether manipulating explanations to be persuasive can influence users’ choices and preferences for options. Here, we report a study (N=231) in which participants were required to make choices among recommended options across three tasks based on the provided explanations. These explanations were presented in a form of natural language. To study the effect of persuasiveness, the explanations were systematically manipulated by incorporating principles of reciprocity, commitment, authority, social proof, liking, and scarcity. In addition, explanations were manipulated for their rationality. The results reveal that the persuasiveness of explanations significantly impacts participants’ recommendation choice behavior, with persuasive explanations being preferred over non-persuasive ones. Moreover, persuasiveness also significantly increased participants’ tendencies toward irrational choices: an increased proportion of selecting a lower utility option when accompanied by a persuasive explanation. The results demonstrate that explanations substantially influence user choice behavior in recommender systems and that users’ choices, as well as the resulting user profiles, can be intentionally or unintentionally shaped by the introduction of persuasive elements in explanations. This could have unprecedented and cumulative effects on the bias of recommendations and can raise ethical concerns about the effect of persuasiveness on users’ long-term behavior in systems integrated with recommendation support. • Examines the influence of persuasive principles on users’ choice-behavior in recommender systems. • Provides empirical evidence on how explanation types affect rational and irrational choices. • Demonstrates the impact of language manipulation in recommendation explanations.