research-article

Social media recommendation based on people and tags

Authors:

Naama Zwerdling,

Erel UzielAuthors Info & Claims

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 194 - 201

https://doi.org/10.1145/1835449.1835484

Published: 19 July 2010 Publication History

Abstract

We study personalized item recommendation within an enterprise social media application suite that includes blogs, bookmarks, communities, wikis, and shared files. Recommendations are based on two of the core elements of social media - people and tags. Relationship information among people, tags, and items, is collected and aggregated across different sources within the enterprise. Based on these aggregated relationships, the system recommends items related to people and tags that are related to the user. Each recommended item is accompanied by an explanation that includes the people and tags that led to its recommendation, as well as their relationships with the user and the item. We evaluated our recommender system through an extensive user study. Results show a significantly better interest ratio for the tag-based recommender than for the people-based recommender, and an even better performance for a combined recommender. Tags applied on the user by other people are found to be highly effective in representing that user's topics of interest.

References

[1]

Amitay, E., Carmel, D., Har'el, N., Soffer, A., Golbandi, N., Ofek-Koifman, S., & Yogev, S. 2009. Social Search and Discovery using a Unified Approach. Proc. HYPERTEXT '09.

Digital Library

[2]

Balabanovic, M. & Shoham, Y. 1997. Fab: Content-based, Collaborative Recommendation. Commun. ACM 40, 3 (Mar. 1997), 66--72.

Digital Library

[3]

Bonhard, P. & Sasse, M. A. 2006. Knowing me, Knowing you - Using Profiles and Social Networking to Improve Recommender Systems. BT Technology Journal 24, 3 (Jul. 2006), 84--98.

Digital Library

[4]

Burke, R. 2002. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12, 4 (2002), 331--370.

Digital Library

[5]

Carmel, D., Zwerdling, N., Guy I., Ofek-Koifman, S., Har'el N., Ronen, I., Uziel, E., Yogev, S., & Chernov, S. 2009. Personalized Social Search based on the User's Social Network. Proc. CIKM '09, 1227--1236.

Digital Library

[6]

Claypool, M., Le, P., Wased, M., & Brown, D. 2001. Implicit Interest Indicators. Proc. IUI '01, 33--40.

Digital Library

[7]

Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. 1999. Combining Content-Based and Collaborative Filters in an Online Newspaper. Workshop on Recommender Systems, SIGIR '99.

[8]

DiMicco, J., Millen, D. R., Geyer, W., Dugan, C., Brownholtz, B., & Muller, M. 2008. Motivations for Social Networking at Work. Proc. CSCW '08, 711--720.

Digital Library

[9]

Farrell, S., & Lau T. 2006. Fringe Contacts: People Tagging for the Enterprise. Workshop on Collaborative Web Tagging, WWW '06.

[10]

Firan, C. S., Nejdl, W., & Paiu, R. 2007. The Benefit of Using Tag--Based Profiles. Proc. LA-WEB '07, 32--41.

Digital Library

[11]

Freyne J., Jacovi, M., Guy I., & Geyer W. 2009. Increasing Engagement through Early Recommender Intervention. Proc RecSys '09, 85--92.

Digital Library

[12]

Golbeck J. 2006. Generating Predictive Movie Recommendations from Trust in Social Networks. Proc. 4th Int. Conf. on Trust Management. Pisa, Italy.

Digital Library

[13]

Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM 35, 12 (Dec. 1992), 61--70.

Digital Library

[14]

Groh, G., & Ehmig, C. 2007. Recommendations in Taste Related Domains: Collaborative Filtering vs. Social Filtering. Proc. GROUP '07, 127--136.

Digital Library

[15]

Guy, I., Jacovi, M., Shahar, E., Meshulam, N., Soroka, V., & Farrell, S. 2008. Harvesting with SONAR: The Value of Aggregating Social Network Information. Proc. CHI '08, 1017--1026.

Digital Library

[16]

Guy I., Ronen I., & Wilcox E. 2009. Do You Know? Recommending People to Invite into Your Social Network. Proc. IUI '09, 77--86.

Digital Library

[17]

Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., & Ofek-Koifman, S. 2009. Personalized recommendation of social software items based on social relations. Proc. RecSys '09, 53--60.

Digital Library

[18]

IBM Social Software for Business - Lotus Connections: http://www-01.ibm.com/software/lotus/products/connections/.

[19]

Kautz, H., Selman, B., & Shah, M. 1997. ReferralWeb: Combining Social Networks and Collaborative Filtering. Commun. ACM 40, 3 (Mar. 1997) 63--65.

Digital Library

[20]

Konstas, I., Stathopoulos, V., & Jose, J. M. 2009. On social networks and collaborative recommendation. Proc. SIGIR '09, 195--202.

Digital Library

[21]

Lerman, K. 2007. Social Networks and Social Information Filtering on Digg. Proc. ICWSM '07.

[22]

Li, X., Guo, L., & Zhao, Y. E. 2008. Tag-based Social Interest Discovery. Proc. WWW '08, 675--684.

Digital Library

[23]

Melville, P., Mooney, R. J., & Nagarajan, R. 2002. Content-boosted collaborative filtering for improved recommendations. Proc. AAAI '02, 187--192.

Digital Library

[24]

Official Digg Blog: http://blog.digg.com/?p=127.

[25]

Official YouTube Blog: http://youtube-global.blogspot.com/2008/06/new-personalized-homepage-and-improved.html

[26]

Pazzani, M.J., & Billsus D. 2007. Content-based recommendation systems. The Adaptive Web, 325--341.

Digital Library

[27]

Ronen, I., Shahar, E., Ur, S., Uziel, E., Yogev, S., Zwerdling, N., Carmel, D., Guy, I., Har'el, N., & Ofek-Koifman, S. 2009. Social networks and discovery in the enterprise (SaND). Proc. SIGIR '09, 836.

Digital Library

[28]

Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. 2002. Methods and Metrics for Cold-start Recommendations. Proc. SIGIR '02, 253--260.

Digital Library

[29]

Sen, S., Vig, J., & Riedl, J. 2009. Tagommenders: Connecting Users to Items through Tags. Proc. WWW '09, 671--680.

Digital Library

[30]

Sinha, R. & Swearingen, K. 2001. Comparing Recommendations Made by Online Systems and Friends. DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.

[31]

Sinha, R. & Swearingen, K. 2002. The Role of Transparency in Recommender Systems. CHI Extended Abstracts '02, 830--831.

Digital Library

[32]

Vatturi, P. K., Geyer, W., Dugan, C., Muller, M., & Brownholtz, B. 2008. Tag-based filtering for personalized bookmark recommendations. Proc. CIKM '08, 1395--1396.

Digital Library

[33]

Vig, J., Sen, S., & Riedl, J. 2009. Tagsplanations: Explaining Recommendations using Tags. Proc. IUI '09, 47--56.

Digital Library

Cited By

Banik SSen SSaha SGhosh S(2024)Towards Reducing Continuous Emotion Annotation Effort During Video Consumption: A Physiological Response Profiling ApproachProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785698:3(1-32)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678569
Shin SJiang ZLim RLyu J(2024)Forecasting the Spread of Sustainability Movement: Computational Analysis on Social Media Messages Promoting Climate ActionsJournal of Current Issues & Research in Advertising10.1080/10641734.2024.236617345:3(282-300)Online publication date: 28-Jun-2024
https://doi.org/10.1080/10641734.2024.2366173
Liu ZLou XLi JLiu G(2024)Enhanced Sub-graph Reconstruction Graph Neural Network for RecommendationApplied Artificial Intelligence10.1080/08839514.2024.235542538:1Online publication date: 12-Jun-2024
https://doi.org/10.1080/08839514.2024.2355425
Show More Cited By

Index Terms

Social media recommendation based on people and tags
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Personalized recommendation of social software items based on social relations
RecSys '09: Proceedings of the third ACM conference on Recommender systems

We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the user's social network. Social network information is collected and ...
Personalization of social media
FDIA'07: Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access

This article describes a framework that captures collaborative tagging systems, and derives from it an overview of user tasks that qualify for personalization in such a system. Major research areas have focused on some of these tasks, but we identify ...
Interactive Social Recommendation
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is beneficial for improving recommendation accuracy, especially when dealing with cold-start users who ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

July 2010

944 pages

ISBN:9781450301534

DOI:10.1145/1835449

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

Copyright © 2010 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

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '10

Sponsor:

SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

Acceptance Rates

SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

191
Total Citations
View Citations
5,520
Total Downloads

Downloads (Last 12 months)220
Downloads (Last 6 weeks)27

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Banik SSen SSaha SGhosh S(2024)Towards Reducing Continuous Emotion Annotation Effort During Video Consumption: A Physiological Response Profiling ApproachProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785698:3(1-32)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678569
Shin SJiang ZLim RLyu J(2024)Forecasting the Spread of Sustainability Movement: Computational Analysis on Social Media Messages Promoting Climate ActionsJournal of Current Issues & Research in Advertising10.1080/10641734.2024.236617345:3(282-300)Online publication date: 28-Jun-2024
https://doi.org/10.1080/10641734.2024.2366173
Liu ZLou XLi JLiu G(2024)Enhanced Sub-graph Reconstruction Graph Neural Network for RecommendationApplied Artificial Intelligence10.1080/08839514.2024.235542538:1Online publication date: 12-Jun-2024
https://doi.org/10.1080/08839514.2024.2355425
Chen RChen JGan X(2024)Multi-view graph contrastive learning for social recommendationScientific Reports10.1038/s41598-024-73336-514:1Online publication date: 30-Sep-2024
https://doi.org/10.1038/s41598-024-73336-5
Haque AIslam NMikalef P(2024)To Explain or Not To Explain: An Empirical Investigation of AI-based Recommendations on Social Media PlatformsElectronic Markets10.1007/s12525-024-00741-z35:1Online publication date: 13-Dec-2024
https://doi.org/10.1007/s12525-024-00741-z
Betello FSiciliano FMishra PSilvestri F(2024)Investigating the Robustness of Sequential Recommender Systems Against Training Data PerturbationsAdvances in Information Retrieval10.1007/978-3-031-56060-6_14(205-220)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_14
Zhou PGao JXie YYe QHua YKim JWang SKim S(2023)Equivariant Contrastive Learning for Sequential RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608786(129-140)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608786
Xie YGao JZhou PYe QHua YKim JWu FKim S(2023)Rethinking Multi-Interest Learning for Candidate Matching in Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608766(283-293)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608766
Qin CZhu HShen DSun YYao KWang PXiong H(2023)Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job InterviewsACM Transactions on Information Systems10.1145/360455242:1(1-32)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1145/3604552
Yu YQi SYang JJiang LNiu DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation ModelsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614925(3030-3039)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614925
Show More Cited By

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