Investigating the Effectiveness of Persuasion Strategies on Recommender Systems

Gkika, Sofia; Lekakos, George

doi:10.1109/smap.2014.37

Cited by 10 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Negative results were obtained when tags were used as a basis for explanations. In addition, the studies that compare different explanation styles (Table 14) confirm the value of social information when designing persuasive explanations [91,203,71]. Only in one single study [254], it turned out that a traditional explanation of the form "people also viewed" was less persuasive than personalised features.…”

Section: Conclusion Reached In User Studiesmentioning

confidence: 94%

A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

296

193

View full text Add to dashboard Cite

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

show abstract

Section: Conclusion Reached In User Studiesmentioning

confidence: 94%

A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

296

193

View full text Add to dashboard Cite

show abstract

“…Furthermore, the explanations can be augmented with Cialdini's influence strategies to enhance the persuasiveness of recommendations. Gkika et al's experiment showed that user acceptance of a recommended item improves when supplemented by a persuasive explanation [30]. Consequently, this thesis investigates the effect of Cialdini's influence(persuasive) strategies on promoting new artists in a music platform that presents songs from both well-known and little-known artists [21].…”

Section: Research Problemmentioning

confidence: 99%

“…From a different perspective, Gkika and Lekakos et al examined the persuasiveness of explanations in recommender systems, using Cialdini's persuasive strategies and concluded that all six principles enhanced users' perception of the recommended items, with Authority and Social Influence being the most effective methods for persuading users to interact with an item. Also, their results showed an increase in the adaption of the recommendation, meaning that a user is more prone to interact with an item that is accompanied by a persuasive explanation [30]. Ren et al focused on the social aspect of recommender systems and introduced a social, collaborative viewpoint regression model.…”

Section: Satisfactionmentioning

confidence: 99%

Investigating the Efficacy of Persuasive Strategies on Promoting Fair Recommendations

Mousavifar

Vassileva

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In presenting this thesis in partial fulfillment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.

show abstract

“…Gkika and Lekakos (2014a , 2014b) investigated the feasibility of using Cialdini’s persuasive principles as explanations in RSs. Particularly, they investigate the effect of using these principles on users’ intention to use a recommendation.…”

Section: Background and Related Workmentioning

confidence: 99%

Users’ Responsiveness to Persuasive Techniques in Recommender Systems

Alslaity

Tran

2021

Front. Artif. Intell.

View full text Add to dashboard Cite

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.

show abstract

Investigating the Effectiveness of Persuasion Strategies on Recommender Systems

Cited by 10 publications

References 26 publications

A systematic review and taxonomy of explanations in decision support and recommender systems

A systematic review and taxonomy of explanations in decision support and recommender systems

Investigating the Efficacy of Persuasive Strategies on Promoting Fair Recommendations

Users’ Responsiveness to Persuasive Techniques in Recommender Systems

Contact Info

Product

Resources

About