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.

Motivational Application for Running: a Participatory Design Approach and the Analysis of Users’ Needs

“…Une approche générale, avec un contenu générique, dans l'utilisation de technologies persuasives est inadéquate pour promouvoir le résultat souhaité et pourrait même réduire l'efficacité de l'intervention [1,4]. Ainsi, il existe un consensus dans la littérature sur la nécessité d'approches adaptées et personnalisées.…”

A panoramic view of personalization based on individual differences in persuasive and behavior change interventions

“…• The popularity of the theories and scales (Figure 7) does not mean they are the dominant theories for all personalized persuasive interventions; The selection of these theories depends on different factors, including the domain area, intervention requirements, target users, and more. For example, if we aim to design a personalized persuasive health system that provides interventions based on user's' awareness of a health issue, then the Health Belief model would be a suitable theory to capture users' differences (Alslaity and Tran, 2021). Besides, users' differences can be captured based on multiple factors and theories.…”

“…However, there has become a consensus in the literature that the one-size-fits-all design of PT is inadequate to promote the desired outcome and could reduce the effectiveness of the intervention (Adnan et al, 2012;Alslaity and Tran, 2021). This consensus has led to a growing interest in finding ways to personalize and tailor PTs and considerable research has investigated how to better design PT to increase motivation and the probability of success (Andrew et al, 2007;Fogg, 2009;Brynjarsdóttir et al, 2012;Weiser et al, 2015;Jalowski et al, 2019;Aldenaini et al, 2020).…”

“…Further work in the education domain (Orji et al, 2019a) demonstrates that using a single strategy is more effective in motivating students and keeping them engaged than incorporating multiple random strategies if the single strategy is tailored to a particular user group. Other research in the eCommerce and recommender systems domain found that personalizing persuasive cues to users' personality traits would enhance users' engagement and acceptance of recommendations (Alslaity and Tran, 2021). In a recent review, Anagnostopoulou et al (2018) examined existing literature on persuasive system implementations in the context of sustainable mobility and found that personalization is an important attribute to improve the impact of the systems and increase the acceptability for real-life usage (e.g., route suggestions based on energy consumption).…”

10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’23)

“…The following year, the topic of evaluation [22,25,44] was the main workshop subject, while foundational topics such as user interfaces [1,27,52], explanation and personalization [40,47] remained stable as in the past. In 2020, new topics emerged in the workshop, such as conversational interfaces [30,31], linguistic features for content analysis [65], and persuasion in recommender systems [7]. However, the most critical role was played by explaination strategies [9,45,48,57].…”