Group Recommender Systems: Beyond Preference Aggregation

Masthoff, Judith; Delić, Amra

doi:10.1007/978-1-0716-2197-4_10

Cited by 20 publications

(17 citation statements)

References 44 publications

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“…They can be divided into individual and group fairness notions. Group fairness ensures fair treatment of similar subjects within the different groups based on protected attributes such as race or gender (Masthoff and Delić, 2012 ). Individual fairness assesses whether individuals are treated fairly by ensuring that similar subjects receive similar decision outcomes (Dwork et al, 2012 ).…”

Section: Terminologymentioning

confidence: 99%

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A review on individual and multistakeholder fairness in tourism recommender systems

2023

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The growing use of Recommender Systems (RS) across various industries, including e-commerce, social media, news, travel, and tourism, has prompted researchers to examine these systems for any biases or fairness concerns. Fairness in RS is a multi-faceted concept ensuring fair outcomes for all stakeholders involved in the recommendation process, and its definition can vary based on the context and domain. This paper highlights the importance of evaluating RS from multiple stakeholders' perspectives, specifically focusing on Tourism Recommender Systems (TRS). Stakeholders in TRS are categorized based on their main fairness criteria, and the paper reviews state-of-the-art research on TRS fairness from various viewpoints. It also outlines the challenges, potential solutions, and research gaps in developing fair TRS. The paper concludes that designing fair TRS is a multi-dimensional process that requires consideration not only of the other stakeholders but also of the environmental impact and effects of overtourism and undertourism.

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Section: Terminologymentioning

confidence: 99%

“…Individual Fairness refers to treating similar individuals in a similar way (Dwork et al, 2012 ). In a group recommender system, this means considering the preferences of all group members fairly and not ignoring any individual's preferences (Masthoff and Delić, 2012 ).…”

Section: Individual Stakeholder Fairness In Trsmentioning

confidence: 99%

A review on individual and multistakeholder fairness in tourism recommender systems

2023

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“…Group recommendation involves recommending items to a collective group rather than individual users, assuming the preferences of group members are known or can be obtained through recommender systems (Felfernig et al, 2018 ; Masthoff and Delić, 2022 ). Aggregating individual user models becomes a challenge in this approach, adding complexity to the recommendation process.…”

Section: Video Recommender Systemsmentioning

confidence: 99%

“…In group video recommendations, the aim is to unite diverse individual user models with different strategies (Masthoff and Delić, 2022 ). For instance, in interactive television, the selection of programs should take into account the satisfaction of the entire group, not just the preferences of a single individual.…”

Section: Video Recommender Systemsmentioning

confidence: 99%

An overview of video recommender systems: state-of-the-art and research issues

Lubos,

Felfernig,

Tautschnig

2023

Front. Big Data

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Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation.

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“…On the other extreme, groups that have big overlaps could be non established or random groups such as people gathered in the same physical place for an event like a club [15]. Members in these groups might not share much in common and they do not necessarily present a defined inner structure for deciding between different options [17]. The same users that are present in a group might behave differently in another group and therefore could be considered as a different user altogether [7].…”

Section: Group Buildingmentioning

confidence: 99%

Enabling Reproducibility in Group Recommender Systems

Silveira

Salamó

Boratto

2022

Frontiers in Artificial Intelligence and Applications

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Reproducibility is a challenging aspect that considerably affects the quality of most scientific papers. To deal with this, many open frameworks allow to build, test, and benchmark recommender systems for single users. Group recommender systems involve additional tasks w.r.t. those for single users, such as the identification of the groups, or their modeling. While this clearly amplifies the possible reproducibility issues, to date, no framework to benchmark group recommender systems exists. In this work, we enable reproducibility in group recommender systems by extending the LibRec library, which stands out as one of the richest, with more than 70 different recommender algorithms, good performance and several evaluation metrics. Specifically, we include several approaches for all the stages of group recommender systems: group formation, group modeling strategies, and evaluation. To validate our framework, we consider a use-case that compares several group building, recommendation, and group modeling approaches.

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Group Recommender Systems: Beyond Preference Aggregation

Cited by 20 publications

References 44 publications

A review on individual and multistakeholder fairness in tourism recommender systems

A review on individual and multistakeholder fairness in tourism recommender systems

An overview of video recommender systems: state-of-the-art and research issues

Enabling Reproducibility in Group Recommender Systems

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