An Altruistic-Based Utility Function for Group Recommendation

Rossi, Silvia; Cervone, Francesco; Barile, Francesco

doi:10.1007/978-3-319-78301-7_2

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…The fifth theme for this variable encompassed a filtering and sorting approach. Figure 24 shows how Rossi proposed in two academic outputs a system in which users have to first choose three movies from a list recommended for the group of viewers and then sort this selection according to their strongest preference [172,173]. In the end, the system considers these decisions to propose a joint solution for the movie recommendation.…”

Section: Achieving a Final Group Consensus In Grsmentioning

confidence: 99%

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

Alvarado

Htun

Jin

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Therefore, we systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains; (2) devices chosen to implement the systems; (3) prototype fidelity; (4) strategies for profile transparency, justification, control, and diversity; (5) strategies for group formation and final group consensus; and, (6) evaluation methods applied in user studies during the design process. Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts.

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Section: Achieving a Final Group Consensus In Grsmentioning

confidence: 99%

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

Alvarado

Htun

Jin

et al. 2022

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Secondly, as recognized in the more recent works [3,15,16], another task is to support the group in their decision-making process, hence not just suggesting an item or a rigid ranked list of items, but rather to truly help the group to reach a joint decision. To increase the effectiveness of these functionalities, the system can make use of various individual, group, interrelationship or even decisionprocess features, such as, individual personalities [17,18,20,21] group type (for example family, close friends, or colleagues), close, highly emotional or loosely coupled relationships within the group [6,9,29], having a well defined, structured decision making-process, or a casual open conversation [7,14].…”

Section: Introductionmentioning

confidence: 99%

GMAP 2022: Workshop on Group Modeling, Adaptation and Personalization

Vinella

Delić

Barile

et al. 2022

Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

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Group modeling adaptation and personalization is an area explored in parallel by two different research communities. On the one hand, the user modeling community focuses on the preferences aggregation problem: how to combine preferences of individuals in a group so as to personalize, adapt, and explain content for this group to consume or experience? On the other hand, the computer-supported collaboration community focuses on the group formation problem: how to construct a group that will work together efficiently to solve a particular task? This area becomes increasingly significant as work becomes more flexible, online, and distributed. The connecting tissue between both communities is the urgent need to design algorithms, whether for recommending group content or group formations, that steer away from top-down algorithmic decision-making, which has proven to stifle user agency and create power inequalities between users and algorithms. The aim of the workshop is, for the first time, to bring together the two communities working on the two sides of Group Recommendations, with an overall goal to rethink group recommendation and shift paradigms from the current algorithm-centric to a user-and group-centric focus.

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Evaluating explainable social choice-based aggregation strategies for group recommendation

Barile

Draws

Inel

et al. 2023

User Model User-Adap Inter

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Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies (N=399 and N=288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.

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An Altruistic-Based Utility Function for Group Recommendation

Cited by 3 publications

References 40 publications

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

A Systematic Review of Interaction Design Strategies for Group Recommendation Systems

GMAP 2022: Workshop on Group Modeling, Adaptation and Personalization

Evaluating explainable social choice-based aggregation strategies for group recommendation

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