research-article

Group recommendations: approaches and evaluation

Authors:

Sang-Wook KimAuthors Info & Claims

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

Article No.: 105, Pages 1 - 6

https://doi.org/10.1145/2701126.2701208

Published: 08 January 2015 Publication History

Abstract

Recommender systems have been an active research topic for the past decade. However, most of previous studies have focused on recommendations to a single user. Recently, as group recommender systems have emerged as an expedient field, several interesting approaches have been proposed. However, despite all of these advances, the current generation of group recommendation approaches still needs further improvements to make more effective recommendations. In this paper, we discuss the limitations of existing group recommendation approaches and present possible developments that could lead to provide better group recommendations. We perform extensive experiments with different group recommendation approaches. The results show that the performance of that group recommendation approaches is limited either by the group type or group size and no single approach consistently performs better than the other approaches. The unavailability of real-life data sets uncovers the doubts for the accuracy of evaluation; the lack of standard terminology/procedure for evaluation also could lead to poor evaluation.

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Cited By

Ceh-Varela ECao HLauw H(2022)Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral GroupsACM Transactions on Intelligent Systems and Technology10.1145/354280413:6(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3542804
Barile FDelic APeska L(2022)Tutorial on Offline Evaluation for Group Recommender SystemsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547371(702-705)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547371
Azzalini DQuintarelli ERabosio ETanca L(2022)FARGO: A Fair, Context-AwaRe, Group RecOmmender SystemAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-031-09316-6_13(143-154)Online publication date: 19-Jun-2022
https://doi.org/10.1007/978-3-031-09316-6_13
Show More Cited By

Index Terms

Group recommendations: approaches and evaluation
1. Information systems
  1. Information retrieval

Recommendations

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Incorporating group recommendations to recommender systems: Alternatives and performance

In collaborative filtering recommender systems recommendations can be made to groups of users. There are four basic stages in the collaborative filtering algorithms where the group's users' data can be aggregated to the data of the group of users: ...

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Published In

cover image ACM Conferences

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

January 2015

674 pages

ISBN:9781450333771

DOI:10.1145/2701126

Conference Chairs:
Dongsoo S. Kim
Indiana University
,
Sang-wook Kim
Hanyang University, Korea
,
General Chairs:
Suk-Han Lee
Sungkyunkwan Univerisity, Korea
,
Lajos Hanzo
University of Southampton, UK
,
Roslan Ismail
Universiti Kuala Lumpur, Malaysia

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2015

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Research-article

Funding Sources

Ministry of Science, ICT and Future Planning
Seoul Creative Human Development Program
Ministry of Education, Science and Technology

Conference

IMCOM '15

Sponsor:

SIGAPP

IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication

January 8 - 10, 2015

Bali, Indonesia

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Overall Acceptance Rate 213 of 621 submissions, 34%

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Citations

Cited By

Ceh-Varela ECao HLauw H(2022)Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral GroupsACM Transactions on Intelligent Systems and Technology10.1145/354280413:6(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3542804
Barile FDelic APeska L(2022)Tutorial on Offline Evaluation for Group Recommender SystemsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547371(702-705)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547371
Azzalini DQuintarelli ERabosio ETanca L(2022)FARGO: A Fair, Context-AwaRe, Group RecOmmender SystemAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-031-09316-6_13(143-154)Online publication date: 19-Jun-2022
https://doi.org/10.1007/978-3-031-09316-6_13
Delić ANguyen TTkalčič M(2022)Group Decision-Making and Designing Group Recommender SystemsHandbook of e-Tourism10.1007/978-3-030-48652-5_57(941-963)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-3-030-48652-5_57
Delić ANguyen TTkalčič M(2020)Group Decision-Making and Designing Group Recommender SystemsHandbook of e-Tourism10.1007/978-3-030-05324-6_57-1(1-23)Online publication date: 2-Apr-2020
https://doi.org/10.1007/978-3-030-05324-6_57-1
Khazaei EAlimohammadi A(2019)Context-Aware Group-Oriented Location Recommendation in Location-Based Social NetworksISPRS International Journal of Geo-Information10.3390/ijgi80904068:9(406)Online publication date: 12-Sep-2019
https://doi.org/10.3390/ijgi8090406
Delic ARicci FNeidhardt J(2019)Preference Networks and Non-Linear Preferences in Group RecommendationsIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352556(403-407)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3350546.3352556
Delic AMasthoff JMitrovic TZhang JChen LChin D(2018)Group Recommender SystemsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209272(377-378)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209272
Delic AMasthoff JNeidhardt JWerthner HMitrovic TZhang JChen LChin D(2018)How to Use Social Relationships in Group RecommendersProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209226(121-129)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209226
Delic ANeidhardt JNguyen TRicci F(2018)An observational user study for group recommender systems in the tourism domainInformation Technology & Tourism10.1007/s40558-018-0106-y19:1-4(87-116)Online publication date: 19-Feb-2018
https://doi.org/10.1007/s40558-018-0106-y
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