Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

Masthoff, Judith

doi:10.1007/978-1-4899-7637-6_22

Cited by 103 publications

(70 citation statements)

References 27 publications

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“…Thus, research on recommender systems is more and more dealing with systems that generate recommendations of items that are supposed to be consumed jointly by a group of people. A detailed overview of the state-of-the-art of GRSs is provided by Masthoff (2015). In order to offer a comprehensive overview of the current and previous research activities in GRSs, different research focuses are separately addressed.…”

Section: Group Recommender Systems State-of-the-artmentioning

confidence: 99%

“…Commonly used aggregation strategies, i.e., methods to aggregate either individuals' recommendations into a group recommendation or individuals' ratings into a group rating, are derived from Social Choice Theory (Masthoff 2015). Some of the most popular aggregation strategies are listed below:…”

Section: Group Recommender Systems State-of-the-artmentioning

confidence: 99%

“…The importance of group recommender systems (GRSs) also has increased due to the social web, where users are not isolated but form interrelated groups of different sizes and compositions. A high number of papers on GRSs have been published (Masthoff 2015) but still, we believe, there is a gap between the current main focus of the research on GRSs and the information search and decision-making support needs of groups.…”

Section: Introductionmentioning

confidence: 99%

“…• Group recommendations techniques have been influenced too strongly by social choice theory (Masthoff 2015) and not enough by group dynamics studies (Forsyth 2014). It is still unclear how a recommender can identify items to suggest in a group decision-making task, if the goal is not simply to aggregate the votes/preferences expressed by the group members.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An observational user study for group recommender systems in the tourism domain

Delić

Neidhardt

Nguyen

et al. 2018

Inf Technol Tourism

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In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decisionmaking in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.

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Section: Group Recommender Systems State-of-the-artmentioning

confidence: 99%

Section: Group Recommender Systems State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An observational user study for group recommender systems in the tourism domain

Delić

Neidhardt

Nguyen

et al. 2018

Inf Technol Tourism

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“…Recommender systems for groups are surveyed in [17]. e authors highlight that the use case of the recommender system greatly affects the design.…”

Section: Group Recommendationsmentioning

confidence: 99%

Where Could We Go?

Ayala-Gómez

Daróczy

Mathioudakis

et al. 2017

Proceedings of the 2017 ACM on Web Science Conference

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Location-Based Social Networks (LBSNs) enable their users to share with their friends the places they go to and whom they go with. Additionally, they provide users with recommendations for Points of Interest (POI) they have not visited before. is functionality is of great importance for users of LBSNs, as it allows them to discover interesting places in populous cities that are not easy to explore. For this reason, previous research has focused on providing recommendations to LBSN users. Nevertheless, while most existing work focuses on recommendations for individual users, techniques to provide recommendations to groups of users are scarce.In this paper, we consider the problem of recommending a list of POIs to a group of users in the areas that the group frequents. Our data consist of activity on Swarm, a social networking app by Foursquare, and our results demonstrate that our proposed Geo-Group-Recommender (GGR), a class of hybrid recommender systems that combine the group geographical preferences using Kernel Density Estimation, category and location features and group check-ins outperform a large number of other recommender systems. Moreover, we nd evidence that user preferences dier both in venue category and in location between individual and group activities. We also show that combining individual recommendations using group aggregation strategies is not as good as building a prole for a group. Our experiments show that (GGR) outperforms the baselines in terms of precision and recall at dierent cutos. CCS CONCEPTS•Information systems →Location based services; Personalization; Recommender systems;Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or aliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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A Group Recommendation Algorithm Based on Dividing Subgroup

Jia

Chen

Shang

2022

Advcd Theory and Sims

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With the rapid development of Internet technology and social networks, traditional personalized recommendation systems have been unable to provide effective recommendation services for groups composed of multiple users. In order to content the needs of group users, the current group recommendation algorithm mainly uses users’ consensus preferences instead of personal preferences for group recommendation, which can only content the preferences of most users and ignore the preferences of a few cold users. In order to improve the fairness of group recommendation, a group recommendation algorithm based on dividing subgroups is proposed. The algorithm introduces the comprehensive trust between users to divide the group into different subgroups, and obtains the initial subgroup recommendation list according to the average strategy. The PageRank idea is used to set the weight of subgroups and assign the number of recommended items to subgroups to generate the group recommendation list. The experimental results show that the algorithm in this paper can content the user's personalized recommendation needs to the greatest extent under the premise of improving the group fair recommendation service.

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Group Recommender Systems: Aggregation, Satisfaction and Group Attributes

Cited by 103 publications

References 27 publications

An observational user study for group recommender systems in the tourism domain

An observational user study for group recommender systems in the tourism domain

Where Could We Go?

A Group Recommendation Algorithm Based on Dividing Subgroup

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