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Users' Responsiveness to Persuasive Techniques in Recommender Systems.

ABSTRACT: Understanding user's behavior and their interactions with artificial-intelligent-based systems is as important as analyzing the performance of the algorithms used in these systems. For instance, in the Recommender Systems domain, the accuracy of the recommendation algorithm was the ultimate goal for most systems designers. However, researchers and practitioners have realized that providing accurate recommendations is insufficient to enhance users' acceptance. A recommender system needs to focus on other factors that enhance its interactions with the users. Recent researches suggest augmenting these systems with persuasive capabilities. Persuasive features lead to increasing users' acceptance of the recommendations, which, in turn, enhances users' experience with these systems. Nonetheless, the literature still lacks a comprehensive view of the actual effect of persuasive principles on recommender users. To fill this gap, this study diagnoses how users of different characteristics get influenced by various persuasive principles that a recommender system uses. The study considers four users' aspects: age, gender, culture (continent), and personality traits. The paper also investigates the impact of the context (or application domain) on the influence of the persuasive principles. Two application domains (namely eCommerce and Movie recommendations) are considered. A within-subject user study was conducted. The analysis of (279) responses revealed that persuasive principles have the potential to enhance users' experience with recommender systems. The study also shows that, among the considered factors, culture, personality traits, and the domain of recommendations have a higher impact on the influence of persuasive principles than other factors. Based on the analysis of the results, the study provides insights and guidelines for recommender systems designers. These guidelines can be used as a reference for designing recommender systems with users' experience in mind. We suggest that considering the results presented in this paper could help to improve recommender-users interaction.

SUBMITTER: Alslaity A

PROVIDER: S-EPMC8297385 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Unhealthy eating behavior is a major public health issue with serious repercussions on an individual's health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users' needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users' perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users' eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora's explanations and recommendation comparisons on users' perception. Our results show that explanations positively influence users' perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.

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Users' Responsiveness to Persuasive Techniques in Recommender Systems.

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