Article

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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... Other studies center their focus on the design of explanations for recommendations, incorporating principles of persuasion. For example, Gkika and Lekakos (2014) have proposed explanations for each persuasion principle. In their user study, participants received movie recommendations accompanied by six distinct explanations and were tasked with evaluating how each explanation might influence their decision to watch the recommended movie. ...
... The results revealed that persuasive explanations that closely aligned with users' preferences had an influence on their acceptance or rejection of the recommended products. As an extension to Gkika and Lekakos (2014); Sofia et al. (2016) have examined the interplay between the six weapons of influence and users' personalities. Similar to the approach in Gkika and Lekakos (2014), the researchers integrated persuasive strategies as explanations alongside recommended products. ...
... As an extension to Gkika and Lekakos (2014); Sofia et al. (2016) have examined the interplay between the six weapons of influence and users' personalities. Similar to the approach in Gkika and Lekakos (2014), the researchers integrated persuasive strategies as explanations alongside recommended products. Their investigation demonstrated that all strategies positively influenced users' acceptance of recommendations and that the effectiveness of different strategies varied depending on users' personalities. ...
Article
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Providing explanations for recommendations has emerged as a critical factor for facilitating effective human decision-making and ensuring user satisfaction. While path-based explanation generation approaches in recommender systems leveraging knowledge graphs have been widely studied, they have overlooked the persuasiveness of explanations. This paper addresses this gap by introducing a personalized approach to measure the persuasiveness of each path explanation, utilizing users’ persuasion profiles derived from their personalities. Subsequently, a re-ranking approach is proposed to optimize the top-n list of recommended products, considering both recommendation utility and the persuasiveness of explanations. Experimental results on a real-world Movie recommendation dataset, employing the recent path reasoning recommender system baselines, demonstrate the effectiveness of our proposed approach in providing a relevant recommendation list with personalized persuasive explanations. Additionally, we investigate the influence of the proposed approach on multiple dimensions of explanation quality beyond persuasiveness. Furthermore, we explore the performance of our approach among user groups characterized by diverse personality traits.
... 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. ...
Article
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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.
... Our results show that both the standard Argumentum Ad Populum and the Group-Ad Populum fallacies, despite their wide use, do not cause any improvement with respect to the neutral condition, thus confirming previous results on the inefficacy of recommendations based on the mere "appeal to the majority" [14,15], but in contrast to other relevant literature [16][17][18][19]). Finally, we found that negative framing, combined with visual accent, is effective in improving the number of clicks on recommended news, consistently with our expectations. ...
... According to Tintarev and Masthoff [28], persuasion is one of the goals of recommendation explanations, i.e., the sentences that are used to annotate recommendations in an attempt to communicate the reasoning process behind recommendation generation and therefore to make the system more transparent [29]. Gkika and Lekakos [16] compared the persuasive effectiveness of explanations based on Cialdini's Influence Principles [30], i.e., reciprocity, scarcity, authority, social proof, liking and commitment, and found that all of them were able to produce a shift in users' behavior, namely in their intention to watch a recommended movie. Zanker and Schoberegger [31] also compared the persuasive effectiveness of three different types of explanations providing the same content, but with different styles: "facts" (e.g., "low altitude, easy distance, very family-friendly"), "logical arguments" (e.g., "low altitude, easy distance therefore very family-friendly"), and "sentences" (e.g., "This route is of low altitude and easy distance, therefore it is very family-friendly."). ...
... This evaluation aims at studying the persuasive effectiveness of recommendation explanations that leverage popularity. As discussed in Section 2.3, not only do many commercial recommender systems and marketplaces suggest popular items, but also scientific research showed that information on item popularity has an impact on users' choices, basically strengthening their preferences for already popular items (see, e.g., [16][17][18][19]). In [14], however, the authors carried out a study in the context of an online bookshop, where they compared different types of affordances aimed at guiding users' behavior, and found that highlighting popular books or annotating them as bestsellers (a form of social navigation) was completely ineffective. ...
Article
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Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user's interest on suggested items leveraging the way news is perceived. We present the results of two evaluations "in the wild", carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing.
... To make it clearer why an item was recommended, explanations can provide additional information and reasons, which can help to improve the overall user experience [2]. While current explanation generation methods have shown to be beneficial in many situations [10,12,41,42,46], the generation of solid and sound natural language explanations, is an ongoing research topic [47]. ...
... The specific effects of explanations in RS were analyzed in different studies: Zanker and Ninaus [46] revealed that explanations could increase the perceived usefulness of the RS as a whole. Following Gkika and Lekakos [12], they can persuade users to choose recommended items. As indicated by Tran et al. [42], explanations can improve user satisfaction. ...
... 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.
... Various studies investigated the specific impacts of explanations in RS: Zanker and Ninaus [51] found that explanations can enhance the perceived usefulness of the RS as a whole. Furthermore, they can influence users to select recommended items [12] and contribute to improved user satisfaction [46]. ...
... Potential goals of explanations are discussed a.o. in Tintarev and Masthoff [62] and Jameson et al. [34]. Examples thereof are efficiency (reducing the time needed to complete a choice task), persuasiveness (exploiting explanations to change a user's choice behavior) [29], effectiveness (proactively helping the user to make higher-quality decisions), transparency (reasons as to why an item has been recommended, i.e., answering why-questions), trust (supporting a user in increasing her confidence in the recommender), scrutability (providing ways to make the user profile manageable), satisfaction (explanations focusing on aspects such as enjoyment and usability), and credibility (assessed likelihood that a recommendation is accurate). Bilgic and Mooney [5] offer a differentiation between explanations that focus on (1) promotion, i.e., convincing users to adopt recommendations, and (2) satisfaction, i.e., to help users make more accurate decisions. ...
Chapter
Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.
... We specifically aim to promote healthy food choices through our justifications, which is novel to food recommender research (Trattner and Elsweiler 2019). The persuasive explanation aim is touted in other domains as useful to convince users to try or buy a recommended item, such as a product on Amazon or a movie on Netflix (Gkika and Lekakos 2014;Tintarev and Masthoff 2012). ...
Article
Full-text available
Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented information is often unrelated to nutrition or difficult to understand. To alleviate this, we present a methodology to generate natural language justifications that emphasize the nutritional content, health risks, or benefits of recommended recipes. Our framework takes a user and two recipes as input and produces an automatically generated natural language justification as output, based on the user’s characteristics and the recipes’ features, following a knowledge-based recommendation approach. We evaluated our methodology in two crowdsourcing studies. In Study 1 (N=502N=502N=502), we compared user food choices for two personalized recommendation approaches, based on either a (1) single-style justification or (2) comparative justification was shown, using a no justification baseline. The recommendations were either popularity-based or health-aware, the latter based on the health and nutritional needs of the user. We found that comparative justification styles were effective in supporting choices for our health-aware recommendations, confirming the impact of our methodology on food choices. In Study 2 (N=504N=504N=504), we used the same methodology to compare the effectiveness of eight different comparative justification strategies. We presented pairs of recipes twice to users: once without and once with a pairwise justification. Results indicated that justifications led to significantly healthier choices for first course meals, while strategies that compared food features and emphasized health risks, benefits, and a user’s lifestyle were most effective, catering to health-related choice motivations.
... Explanations in RS influence users by persuading them to consume recommended items [13], increasing the perceived usefulness of the RS [14], and contributing to overall user satisfaction [15]. Domain-specific content data enhances explanation effectiveness, while better transparency leads to higher user satisfaction [12]. ...
Preprint
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Recommender systems assist users in decision-making, where the presentation of recommended items and their explanations are critical factors for enhancing the overall user experience. Although various methods for generating explanations have been proposed, there is still room for improvement, particularly for users who lack expertise in a specific item domain. In this study, we introduce the novel concept of \textit{consequence-based explanations}, a type of explanation that emphasizes the individual impact of consuming a recommended item on the user, which makes the effect of following recommendations clearer. We conducted an online user study to examine our assumption about the appreciation of consequence-based explanations and their impacts on different explanation aims in recommender systems. Our findings highlight the importance of consequence-based explanations, which were well-received by users and effectively improved user satisfaction in recommender systems. These results provide valuable insights for designing engaging explanations that can enhance the overall user experience in decision-making.
... Such "embedding" is inevitably based on various principles and strategies of persuasion, which applied in advertising communication are likely to cause targeting of consumer decisions to advertised products as desired or necessary (Sofia, Marianna, George & Panos, 2016, cited by: Alslaity & Tran, 2020. Some studies provide evidence that persuasive statements are likely to work as explanations for the choice of advertised objects (Gkika & Lekakos, 2014). ...
Article
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This investigation has two aims: to study the susceptibility of young consumers to persuasion principles of Cialdini and to identify which principles achieve a powerful effect of memory through advertising in consumers of different genders and ages. The instrument used is the adapted and modified STPS questionnaire, developed by Kaptein el at., as well as the subjective judgments of respondents about the degree to which they remember advertisements based on persuasion principles. The results of the regression analysis show that social proof is the only principle of persuasion whose persuasive power does not correspond to that of three principles: principle of reciprocity, principle of commitment and consistency and principle of unity. Practical application of principle of commitment and consistency and principle of reciprocity is more persuasive in uniform advertising when gender and age characteristics of the target groups are not taken into account. The causal link between the principle of social proof and memorization of advertising is established, where persuasion is done in a peripheral route, although consumers are susceptible to other principles. Ranking of the principles has established that the principle of commitment and consistency achieves greatest effectiveness in both genders, while the principle of reciprocity is most effective for women and the principle of authority - for men. Regarding the age groups, the following principles are observed: 18 to 20 - principle of social proof; 21 to 24 - principle of unity, and 25 to 35 - principle of commitment and consistency. Different consumer susceptibility to persuasion can be achieved in the studied age groups on a peripheral route with higher efficiency in women. This study is useful for organizations that offer different products and services through advertising.
... Sociable conversational recommender systems (CRS) aim to build rapport with users while interacting them in natural language, and thereby influence recommendation dialogs positively with the users [1]. In this regard, in addition to item databases to recommend from, CRS utilize recorded dialogs usually collected between humans where one plays the role of a recommendation-seeker and the other as human-recommender, see e.g., [2]. ...
Preprint
Full-text available
Conversational recommender systems (CRS) that interact with users in natural language utilize recommendation dialogs collected with the help of paired humans, where one plays the role of a seeker and the other as a recommender. These recommendation dialogs include items and entities to disclose seekers' preferences in natural language. However, in order to precisely model the seekers' preferences and respond consistently, mainly CRS rely on explicitly annotated items and entities that appear in the dialog, and usually leverage the domain knowledge. In this work, we investigate INSPIRED, a dataset consisting of recommendation dialogs for the sociable conversational recommendation, where items and entities were explicitly annotated using automatic keyword or pattern matching techniques. To this end, we found a large number of cases where items and entities were either wrongly annotated or missing annotations at all. The question however remains to what extent automatic techniques for annotations are effective. Moreover, it is unclear what is the relative impact of poor and improved annotations on the overall effectiveness of a CRS in terms of the consistency and quality of responses. In this regard, first, we manually fixed the annotations and removed the noise in the INSPIRED dataset. Second, we evaluate the performance of several benchmark CRS using both versions of the dataset. Our analyses suggest that with the improved version of the dataset, i.e., INSPIRED2, various benchmark CRS outperformed and that dialogs are rich in knowledge concepts compared to when the original version is used. We release our improved dataset (INSPIRED2) publicly at https://github.com/ahtsham58/INSPIRED2.
... 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]. ...
Article
Full-text available
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. ...
Conference Paper
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Similar to single user decisions, group decisions can be affected by decision biases. In this paper we analyze anchoring effects as a specific type of decision bias in the context of group decision scenarios. On the basis of the results of a user study in the domain of software requirements prioritization we discuss results regarding the optimal time when preference information of other users should be disclosed to the current user. Furthermore, we show that explanations can increase the satisfaction of group members with various aspects of a group decision process (e.g., satisfaction with the decision and decision support quality).
Thesis
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O presente trabalho avalia de que forma as características persuasivas das plataformas de comércio eletrónico influenciam a experiência de compra online dos consumidores. A interseção de persuasão com tecnologia consubstancia-se em sistemas computadorizados interativos que aplicam intencionalmente os princípios psicológicos da persuasão para induzir alterações nas atitudes e comportamentos dos indivíduos. O desenvolvimento destes sistemas é designado de design persuasivo. O enquadramento teórico cruza persuasão, tecnologia e design enquanto campos de investigação, fornecendo as bases para a compreensão dos determinantes do comportamento humano, dos processos para a sua modificação, dos efeitos contextuais da tecnologia, e dos quadros conceptuais para a sua análise e desenvolvimento. Delimita-se depois o campo de aplicação: identificam-se as características do comércio eletrónico, das suas plataformas e dos seus promotores; e mapeiam-se as especificidades da jornada de compra e os determinantes do comportamento dos consumidores. O estudo empírico caracteriza e analisa a implementação de um conjunto de 35 princípios persuasivos (estratégias de design baseadas em impulsos do comportamento humano) em 160 interfaces de websites e aplicações móveis — Airbnb, Amazon, Apple, Booking.com, eBay, Farfetch, Gearbest e Nike — e determina as diferenças decorrentes de especificidades tecnológicas e comportamentais dos consumidores. A análise de 5.600 instâncias de princípios persuasivos, ao longo de quatro tipologias de interfaces, permitiu obter um conjunto substancial de resultados: verificar a disseminação e profusão das estratégias persuasivas; confirmar uma significativa variação contextual na sua incidência e materializações; caracterizar táticas e descrever ilações dos seus efeitos. Os contributos deste estudo são inovadores em diversos sentidos: é precursor na análise e comparação do design persuasivo das plataformas atendendo a diferentes pontos de contacto (websites e aplicações móveis) e dispositivos (computadores, tablets e smartphones); é o primeiro a propor um mapeamento dos componentes persuasivos por casos puros (intencionais, inequívocos e/ou unânimes) e limite (desintencionais, ambíguos e/ou discutíveis) para cada princípio persuasivo; é o primeiro a mapear a incidência e materialização destes princípios entre as tipologias de interfaces mais relevantes para a experiência de compra online; é o primeiro a utilizar simultaneamente na sua análise os dois modelos mais relevantes (PSD e Cialdini), traçando relações entre estes; é o primeiro a efetuar uma análise com a última revisão do modelo de Cialdini (ao qual foi adicionado o princípio de unidade).
Chapter
Recommender systems have become an inseparable part of our daily life, like listening to music based on recommender playlists or browsing through the recommended shopping list online. Fairness in such recommender systems has gained lots of attention considering provider and system objectives along with end-user satisfaction. However, often there are trade-offs between the objectives of different stakeholders. For instance, fairness for providers can be defined as ensuring the same exposure for all providers [7]. However, less popular providers might not satisfy users as much as widely-known providers; therefore, user satisfaction might decrease significantly. Consequently, there is a need to explore methods to promote recommendations from less-known providers more effectively. Previous studies have shown that explanations and persuasive explanations are beneficial for increasing user acceptance of recommended items. However, there has been little work investigating explanations for a fairness objective. Here, we study the effect of persuasive strategies for promoting items included for the recommender’s fairness objective in a music platform. Results show empirical evidence of higher user satisfaction for the items accompanied by explanations. Our findings could guide the user interface design of two-sided marketplaces leading to a better user satisfaction rate. KeywordsPersuasive technologiesFair recommendationExplainable recommendationMulti-stakeholder recommendation
Article
Prior work has found that computers can effectively use six persuasion strategies characterized by Cialdini to influence people’s intentions and behaviors. However, researchers are yet to examine the effectiveness of Cialdini’s persuasion strategies with virtual humans. Virtual humans provide a human representation to computers, which influences how people respond to persuasion attempts from computers. To evaluate Cialdini’s persuasion strategies with virtual humans, we conducted an online study (N=183), where a virtual human promoted a coping skill for good mental health using Cialdini’s six persuasion strategies. Our results reveal that strategies that persuaded users by increasing the feeling of liking and reciprocity towards the virtual human were most successful in changing user intentions to perform the recommended behavior. Furthermore, the relative effectiveness of strategies that involved persuasion using expertise and normative beliefs varied for different user personality types. We draw conclusions and design implications for using Cialdini’s persuasion strategies with virtual humans.
Conference Paper
Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user’s characteristics and the recipes’ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe’s food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices.
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Negative impacts produced by transportation sector have increased in parallel with the increase of urban mobility. In this paper, we introduce GreenCommute, a novel recommendation system which can facilitate commuters to take public friendly commute options, while provide support to alleviate the external cost in society, such as traffic pollution, congestion and accidents. In the meanwhile, a rewarding mechanism for persuading commuters is embedded in the proposed approach for balancing the conflict between personal needs and social aims. The allocation of reward values also takes users’ influential degrees in the social network into consideration. Experimental results show that the GreenCommute can promote public friendly commute options more effectively in comparison to the traditional recommendation system.
Chapter
Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.
Article
The paper examines the persuasive effect of explanations on the intention of users to use recommended items. For the needs of the present study, a movie Recommender System was built so as to elicit participants' preferences about movies and then a movie is recommended in order to investigate the effect of the application of Cialdini's Influence Principle(s), implemented as recommendation explanations, on the users' intention to watch the movie. Surprisingly, the experimental results of the study suggest that if a Recommender System provides persuasive explanations for a product/service that is close to a user's preferences and tastes then it changes his/her behaviour.
Article
Behavior change support systems (BCSS) research is an evolving area. While the systems have been demonstrated to work to the effect, there is still a lot of work to be done to better understand the influence mechanisms of behavior change, and work out their influence on the systems architecture. The papers of the second BCSS workshop aim at filling this gap. They test existing influence strategies and suggest new ones, develop evaluation methods of influence strategies, and introduce systems architectures that support novel influence strategies.
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When recommender systems present items, these can be accompanied by explanatory information. Such explanations can serve seven aims: effectiveness, satisfaction, transparency, scrutability, trust, persuasiveness, and efficiency. These aims can be incompatible, so any evaluation needs to state which aim is being investigated and use appropriate metrics. This paper focuses particularly on effectiveness (helping users to make good decisions) and its trade-off with satisfaction. It provides an overview of existing work on evaluating effectiveness and the metrics used. It also highlights the limitations of the existing effectiveness metrics, in particular the effects of under- and overestimation and recommendation domain. In addition to this methodological contribution, the paper presents four empirical studies in two domains: movies and cameras. These studies investigate the impact of personalizing simple feature-based explanations on effectiveness and satisfaction. Both approximated and real effectiveness is investigated. Contrary to expectation, personalization was detrimental to effectiveness, though it may improve user satisfaction. The studies also highlighted the importance of considering opt-out rates and the underlying rating distribution when evaluating effectiveness.
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A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users’ perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS’s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users’ adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system’s recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system’s overall perceptive qualities and how these qualities influence users’ behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.
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This article describes the use of personalized short text messages (SMS) to reduce snacking. First, we describe the development and validation (N = 215) of a questionnaire to measure individual susceptibility to different social influence strategies. To evaluate the external validity of this Susceptibility to Persuasion Scale (STPS) we set up a two week text-messaging intervention that used text messages implementing social influence strategies as prompts to reduce snacking behavior. In this experiment (N = 73) we show that messages that are personalized (tailored) to the individual based on their scores on the STPS, lead to a higher decrease in snacking consumption than randomized messages or messages that are not tailored (contra-tailored) to the individual. We discuss the importance of this finding for the design of persuasive systems and detail how designers can use tailoring at the level of social influence strategies to increase the effects of their persuasive technologies.
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We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the user's social network. Social network information is collected and aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the user's familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and their relationship to the user and to the item. Evaluation, based on an extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations. In addition, an important instant effect of explanations is found - interest rate in recommended items increases when explanations are provided.
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Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transparency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.
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Recommender Systems (RSs) help users search large amounts of digital contents and services by allowing them to identify the items that are likely to be more attractive or useful. RSs play an important persuasion role, as they can potentially augment the users’ trust towards in an application and orient their decisions or actions towards specific directions. This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. First, we investigate if a design property of RSs, defined by the statistically measured quality of algorithms, is a reliable predictor of their potential for persuasion. This factor is measured in terms of perceived quality, defined by the overall satisfaction, as well as by how users judge the accuracy and novelty of recommendations. For our purposes, we designed an empirical study involving 210 subjects and implemented seven full-sized versions of a commercial RS, each one using the same interface and dataset (a subset of Netflix), but each with a different recommender algorithm. In each experimental configuration we computed the statistical quality (recall and F-measures) and collected data regarding the quality perceived by 30 users. The results show us that algorithmic attributes are less crucial than we might expect in determining the user’s perception of an RS’s quality, and suggest that the user’s judgment and attitude towards a recommender are likely to be more affected by factors related to the user experience. Second, we explore the persuasiveness of RSs in the context of large interactive TV services. We report a study aimed at assessing whether measurable persuasion effects (e.g., changes of shopping behavior) can be achieved through the introduction of a recommender. Our data, collected for more than one year, allow us to conclude that, (1) the adoption of an RS can affect both the lift factor and the conversion rate, determining an increased volume of sales and influencing the user’s decision to actually buy one of the recommended products, (2) the introduction of an RS tends to diversify purchases and orient users towards less obvious choices (the long tail), and (3) the perceived novelty of recommendations is likely to be more influential than their perceived accuracy. Overall, the results of these studies improve our understanding of the persuasion phenomena induced by RSs, and have implications that can be of interest to academic scholars, designers, and adopters of this class of systems.
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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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There is increasing awareness in recommender systems research of the need to make the recommendation process more transparent to users. Following a brief review of existing approaches to explanation in recommender systems, we focus in this paper on a case-based reasoning (CBR) approach to product recommendation that offers important benefits in terms of the ease with which the recommendation process can be explained and the system’s recommendations can be justified. For example, recommendations based on incomplete queries can be justified on the grounds that the user’s preferences with respect to attributes not mentioned in her query cannot affect the outcome. We also show how the relevance of any question the user is asked can be explained in terms of its ability to discriminate between competing cases, thus giving users a unique insight into the recommendation process.
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This study replicated and expanded on earlier research on gender differences in the evaluation of computer-mediated persuasive messages. Participants discussed a counter-attitudinal topic with a same-gender confederate. Those participants made to feel a sense of shared identity (high oneness) with the communicator were the most favorable toward the proposal whereas those participants made to feel a distinct identity (low oneness) were the least favorable. However, the results were different for men and women depending on communication modality. Cognitive responses indicated that men engaged in a more rational evaluation of the persuasive message in the email condition, even when the communicator and recipient did not share an identity. Thus, one implication of this research is that email may be an effective route for men to use for interacting with one another if they share no mutual identity.
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This article describes the basic mechanisms underlying persuasion highlighting the role of a recently discovered new process—called self‐validation. Unlike previous mechanisms in attitude change that focus on primary or first‐order cognition, this new process emphasizes secondary or meta‐cognition. The key notion of self‐validation is that generating thoughts is not sufficient for them to have an impact on judgment. Rather, one must also have confidence in them. We review research revealing that this new mechanism can account for some already established outcomes in persuasion, but by a different process than postulated previously, as well as for some new findings. Specifically, we describe how source (e.g., credibility), recipient (e.g., bodily responses), message (e.g., matching), and context (e.g., repetition) variables can influence persuasion by affecting thought‐confidence. We also describe how establishing a basic mechanism such as self‐validation can provide a novel framework for understanding a variety of additional phenomenon in the domain of persuasion and beyond.
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Most of recommender systems have serious difficulties on providing relevant services to the “short-head” users who have shown intermixed preferential patterns. In this paper, we assume that such users (which are referred to as long-tail users) can play an important role of information sources for improving the performance of recommendation. Attribute reduction-based mining method has been proposed to efficiently select the long-tail user groups. More importantly, the long-tail user groups as domain experts are employed to provide more trustworthy information. To evaluate the proposed framework, we have integrated MovieLens dataset with IMDB, and empirically shown that the long-tail user groups are useful for the recommendation process.