More Web Proxy on the site http://driver.im/

short-paper

Interface and interaction design for group and social recommender systems

Author:

Yu ChenAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 363 - 366

https://doi.org/10.1145/2043932.2044007

Published: 23 October 2011 Publication History

Abstract

Group and social recommender systems aim to recommend items of interest to a group or a community of people. The user issues in such systems cannot be addressed by examining the satisfaction of their members as individuals. Rather, group satisfaction should be studied as a result of the interaction and interface methods that support group dynamics and interaction. In this paper, we survey the state-of-the-art in user experience design of group and social recommender systems. We further apply the techniques used in the current recommender systems to GroupFun, a music social group recommender system. After presenting the interface and interaction characteristics of GroupFun, we further analyze the design space and propose areas for future research in pursuit of an affective recommender.

References

[1]

Jameson, A., 2004. More than the sum of its members: Challenges for group recommender systems. In Proceedings of the International Working Conference on Advanced Visual Interfaces. Pp. 48--54.

Digital Library

[2]

McCarthy, K., Mcginty, L., Smyth, B. and Salamó, M. The Needs of the Many: A Case-Based Group Recommender System. In ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 196--210

Digital Library

[3]

Ellis, C., J. Gibbs, S. and Rein, G. 1991. Groupware: some issues and experiences. Commun. ACM 34, 1 (January 1991), 39--58.

Digital Library

[4]

Crossen, A., Budzik, J. and Hammond, K. 2002. Flytrap: intelligent group music recommendation. In Proceedings of the 7th international conference on Intelligent user interfaces, January 13--16, 2002, San Francisco, USA.

Digital Library

[5]

Cosley, D., A. Konstan, J. and Riedl, J. 2001. PolyLens: a recommender system for groups of users, Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work, p.199--218, September 16-20, 2001, Bonn, Germany.

Digital Library

[6]

McCarthy, K., Salamo, M., Coyle, L., McGinty, L., Smyth, B., and Nixon, P., 2005. CATS: A Synchronous Approach to Collaborative Group Recommendation.

[7]

Jameson, A., Baldes, S., and Kleinbauer, T. 2004. Two methods for enhancing mutual awareness in a group recommender system. In Proceedings of the working conference on Advanced visual interfaces (AVI '04). ACM, New York, NY, USA, 447--449.

Digital Library

[8]

Ardissono, L., Goy, A., Petrone, G., Segnan, M. and Torasso, P. 2003 INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices. Applied Artificial Intelligence 17(8-9) (2003) 687--714.

[9]

Guy, I., Zwerdling, N., Carmel, D., Ronen, I. Uziel, E., Yogev, S., and Ofek-Koifman, S. 2009a. Personalized recommendation of social software items based on social relations. In Proceedings of the third ACM conference on Recommender systems (RecSys '09). ACM, New York, NY, USA, 53--60.

Digital Library

[10]

Chen, J., Geyer, W., Dugan, C., Muller, M. and Ido Guy. 2009. Make new friends, but keep the old: recommending people on social networking sites. In Proceedings of the 27th international conference on Human factors in computing systems (CHI '09). ACM, New York, NY, USA, 201--210.

Digital Library

[11]

Guy, I., Jacovi, M., Perer, A., Ronen, I., and Uziel, E. 2010a. Same places, same things, same people?: mining user similarity on social media. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (CSCW '10). ACM, New York, NY, USA, 41--50.

Digital Library

[12]

Guy, I., Ronen, I. and Wilcox, E. 2009. Do you know?: recommending people to invite into your social network. In Proceedings of the 13th international conference on Intelligent user interfaces, February 08-11, 2009, Sanibel Island, Florida, USA

Digital Library

[13]

Yu, Z., Zhou, X., Hao and Y., Gu, J. 2006. TV Program Recommendation for Multiple Viewers Based on user Profile Merging. In User Modeling and User-Adapted Interaction, v.16 n.1, p.63--82, March 2006.

Digital Library

[14]

McCarthy J. and Anagnost T. 1998. MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In Proceedings of the 1998 ACM conference on Computer supported cooperative work, p.363--372.

Digital Library

[15]

McCarthy, J. F., 2002. Pocket Restaurant Finder: A Situated Recommender System for Groups. Accenture Technology Labs.

[16]

Kudenko, D., Bauer, M., and Dengler, D., 2003. Group Decision Making Through Mediated Discussions. In Proceedings of the 9th International Conference on User Modeling.

Digital Library

[17]

Ludford, P. J., D. Cosley, Frankowski, D. and Terveen, L. 2004. Think different: increasing online community participation using uniqueness and group dissimilarity. Proceedings of the SIGCHI conference on Human factors in computing systems. Vienna, Austria, ACM: 631--638.

Digital Library

[18]

Anderson, C and Kilduff, G. J. 2009. Why do dominant personalities attain influence in face-to-face groups? The competence-signaling effects of trait dominance. Journal of Personality and Social Psychology, Vol 96(2), Feb 2009, 491--503.

[19]

Barasade, S.G. "The Ripple Effect: Emotional Contagion and Its Influence on Group Behavior." Admin Sci Qtrly. 47(2002), pp. 644--675

Cited By

Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555161
van Capelleveen GAmrit CYazan DZijm H(2022)The recommender canvasExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.04.001129:C(97-117)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2019.04.001
Dhruv ABakal J(2021)Investigating the Role of User Experience and Design in Recommender Systems: A Pragmatic ReviewMobile Computing and Sustainable Informatics10.1007/978-981-16-1866-6_11(161-170)Online publication date: 23-Jul-2021
https://doi.org/10.1007/978-981-16-1866-6_11
Show More Cited By

Index Terms

Interface and interaction design for group and social recommender systems

Recommendations

Enhancing memory-based collaborative filtering for group recommender systems

Enhancing memory-based collaborative filtering techniques for group recommender systems by resolving the data sparsity problem.Comparing the proposed method's accuracy with basic memory-based techniques and latent factor model.Makeing accurate ...
Interface Design to Mitigate Inflation in Recommender Systems
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Recommendation systems rely on user-provided data to learn about item quality and provide personalized recommendations. An implicit assumption when aggregating ratings into item quality is that ratings are strong indicators of item quality. In this work, ...
Using Groups of Items for Preference Elicitation in Recommender Systems
CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing

To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

Copyright © 2011 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]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

RecSys '11

Sponsor:

SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
757
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)1

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555161
van Capelleveen GAmrit CYazan DZijm H(2022)The recommender canvasExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.04.001129:C(97-117)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2019.04.001
Dhruv ABakal J(2021)Investigating the Role of User Experience and Design in Recommender Systems: A Pragmatic ReviewMobile Computing and Sustainable Informatics10.1007/978-981-16-1866-6_11(161-170)Online publication date: 23-Jul-2021
https://doi.org/10.1007/978-981-16-1866-6_11
Katarya RVerma O(2016)Recent developments in affective recommender systemsPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2016.05.046461(182-190)Online publication date: Nov-2016
https://doi.org/10.1016/j.physa.2016.05.046
Jahanian AJahanian A(2016)Design Mining Color SemanticsQuantifying Aesthetics of Visual Design Applied to Automatic Design10.1007/978-3-319-31486-0_3(15-55)Online publication date: 21-Jun-2016
https://doi.org/10.1007/978-3-319-31486-0_3

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents