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The persuasive role of Explanations in Recommender Systems

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Abstract

Explanations in Recommender Systems can operate like motivators influencing consumers to purchase the recommended items. In this study, we rely upon the well established and verified framework of Cialdini's Influence Principles in order to enrich recommendations with explanations and examine their effect on the persuasive power of recommendations. The results of the experiment revealed that all six Influence Principles positively affect users' perception about the recommended movie while Authority and Social Proof seem to be the more effective ones. These findings indicate that a user's intention to consume a recommended good is increased if the item is accompanied with a persuasive explanation.

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... Based on the results, a designer can provide multiple implementations (each targeting different users' groups) of the same system. Third, many RSs use explanations to clarify why a particular item is recommended to the user (Gkika and Lekakos, 2014a). These explanations can be more effective if they encompass persuasion cues. ...
... The work that discusses the effect of persuasive principles (such as Cialdini's six principles) in the context of recommender systems is relatively limited (Gkika and Lekakos, 2014a). However, the research in this direction is gaining increasing attention. ...
... These observations indicate that the majority of the participants believe that any of the six persuasive principles can influence their decisions to some extent. This is in line with the conclusion of Gkika and Lekakos (2014a), which indicated that accompanying persuasive explanations with a recommendation increases users' acceptance of that recommendation. ...
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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.
... Gkika and Lekakos [12] investigated the impact of implementing the six weapons of influence on users' intention to use a recommendation. The paper suggested an explanation to represent each strategy. ...
... As an extension to Gkika and Lekakos' study [12], Sofia et al. [9] investigated the influence of persuasive strategies on users' intention to use a recommended item if it is preferred to the user. Particularly, the authors discussed the six weapons of influence in conjunction with users' personalities; Similar to the previous study, the persuasive strategies were also incorporated into the systems as explanations along with the recommended items. ...
Chapter
Persuasive technology is gaining increasing attention nowadays. Researchers have proposed several approaches to support technology with persuasive capabilities inspired originally from the domain of social sciences. Cialdini’s six persuasive principles, known as the “six weapons of influence,” is an example of such techniques that are widely deployed in the persuasive technology domain. However, the literature lacks studies that asses the relationship between the domain, in which a persuasive technology is applied, and how the former is actually affecting the degree of persuasion achieved by Cialdini’s six persuasive principles. To bridge this gap, we investigate the effect of the application domain on users’ susceptibility to Cialdini’s principles. Two application domains were considered, namely an e-commerce recommender system and a movie recommender system. A within-subject study is conducted, and a total of 107 responses were collected. The results show that when using the same persuasive technique, the nature of the application domain affects the way users got persuaded by that technique. Hence, our findings suggest that the application area, as a contextual dimension, is an important factor that should be taken into consideration when designing persuasive systems.
... Bilgic and Mooney [2005], for example, argue that transparency -as provided by explanations -in general has a persuasive effect because well-informed users are more likely to make a purchase decision. On the other hand, explanations can be used to push certain items, e.g., by deliberately omitting information, focusing on less relevant details, or presenting explanations that persuade users to choose an option that is not optimal for them but profitable for the provider [Tintarev and Masthoff 2007;Gkika and Lekakos 2014]. Gkika and Lekakos [2014] investigated comparably simple forms of explanations which were not related to the inner workings of a recommendation strategy with respect to their persuasiveness. ...
... On the other hand, explanations can be used to push certain items, e.g., by deliberately omitting information, focusing on less relevant details, or presenting explanations that persuade users to choose an option that is not optimal for them but profitable for the provider [Tintarev and Masthoff 2007;Gkika and Lekakos 2014]. Gkika and Lekakos [2014] investigated comparably simple forms of explanations which were not related to the inner workings of a recommendation strategy with respect to their persuasiveness. Their strategies included, e.g., the presentation of messages related to scarcity ("Soon to be discontinued") or commitment ("You should try new things"). ...
Article
Automated recommendations have become a ubiquitous part of today’s online user experience. These systems point us to additional items to purchase in online shops, they make suggestions to us on movies to watch, or recommend us people to connect with on social websites. In many of today’s applications, however, the only way for users to interact with the system is to inspect the recommended items. Often, no mechanisms are implemented for users to give the system feedback on the recommendations or to explicitly specify preferences, which can limit the potential overall value of the system for its users. Academic research in recommender systems is largely focused on algorithmic approaches for item selection and ranking. Nonetheless, over the years a variety of proposals were made on how to design more interactive recommenders. This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems. We cover existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process. Throughout the work, we furthermore discuss examples of real-world systems and outline possible directions for future works.
... Sociable conversational agents build rapport with users, in order to gain trust and favor from them. Social science researchers believe that the rapport influence a more persuasive recommendation to successfully suggest an item that satisfies user needs (Yoo et al., 2012;Gkika and Lekakos;Pecune et al., 2019;Gretzel and Fesenmaier, 2006). ...
... Providing "appropriate" recommendations refers to what to recommend, as well as how and when to recommend an item. An online experiment on a realworld platform discusses the persuasive role of explanations suggesting that they are an essential piece of functionality of a recommendation system, since it enhances user's perception and engagement [37]. Explanations can also enhance user's decision making regarding whether to choose the recommended item, as well as to assess the potential benefits of following the recommendation [38]. ...
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BACKGROUND: There is strong evidence that cognitive skills and executive functions are skills that children need in order to successfully learn in school. Although executive function disorders are not considered a learning disability, weaknesses in executive functioning are often observed in students with learning disabilities or ADHD. Cognitive games are a type of educational games which focus on enhancing cognitive functioning in children with different profiles of cognitive development, including students with neurocognitive and/or learning disabilities. Self-regulation and metacognitive skills also play an important role in academic performance. OBJECTIVE: In this work, we highlight the need of monitoring and supporting metacognitive skills (self-regulation) in the context of a cognitive training game. We propose a system for self-regulated cognitive training for children which supports metacognitive strategies allowing the child to reflect on their own progress, weaknesses and strengths, self-arrange the training content, and thus to promote their self-regulated learning skills. METHODS: We provide a narrative review of research in cognitive training, self-regulated learning and explainable recommendation systems for children in educational settings. RESULTS AND CONCLUSIONS: Based on the review, an experimental testbed is proposed to explore how transparency, explainability and persuasive strategies can be used to promote self-regulated learning skills in children, considering individual differences on learning abilities, preferences, and needs.
... Explanations in RS improve users' trust in the recommended products [3,21,23,27]. Besides, explanations can persuade users to buy a recommended product [13,29]. Content-based recommenders are more transparent, or understandable for users, as they can make use of explanations based on the user profile or the products with similar features [17]. ...
Chapter
Recommender systems are useful to find relevant products for a certain user. Some recommender techniques based on models, for example, Matrix Factorization, act as a black box for users. Explanations for recommender systems are useful to make recommendations more effective and help the users to trust the system and understand why certain items have been recommended. In this paper, we propose a post-hoc model-agnostic explanation system for MF recommendations based on Case-Based Reasoning and Formal Concept Analysis. We have conducted an experimental evaluation with real users to define what are the most useful explanation features that allow users a better understanding of the system recommendation.
... An experiment has been conducted by Herse et al. [19] in the context of restaurant recommendation implied that human-like features of agents might support boosting RS persuasiveness. Other studies have investigated the effectiveness of implementing some persuasive strategies as explanations for recommendations on users' intention to use a recommendation (e.g., [11], and [20]). The results proved that incorporating persuasive explanations to recommendations that are close to users' preferences will affect their behavior in terms of accepting/rejecting a recommended item. ...
... Explanations can have a significant impact on the way that items are perceived/evaluated and -as a consequence -on the corresponding decision. Thus, explanations play an important role in recommender systems [8,21], for example, a digital camera will be purchased or not, a movie will be watched or not, a car feature will we included or not, a project proposal will be accepted or not, and a software requirement will be regarded as important or not. Stettinger et al. [18] analyze the impact of argument orderings of item explanations on the decision outcome, Felfernig et al. [6] and Pu et al. [16] show the (positive) influence of explanations on a user's trust in recommender systems, and Herlocker et al. [10] discuss different explanation-relevant dimensions in recommender systems where beside justification, user involvement, and education, acceptance is mentioned as a major relevant factor. ...
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This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”, and suggest guidelines as how to best evaluate this. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing systems, and how these relate to evaluations with live recommender systems. We also discuss how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.
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