research-article

Personalized recommendation of social software items based on social relations

Authors:

Naama Zwerdling,

Shila Ofek-KoifmanAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 53 - 60

https://doi.org/10.1145/1639714.1639725

Published: 23 October 2009 Publication History

Abstract

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 aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the user's familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and their relationship to the user and to the item. Evaluation, based on an extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations. In addition, an important instant effect of explanations is found - interest rate in recommended items increases when explanations are provided.

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. HT'09, 199--208.

Digital Library

[2]

Arguello, J., Elsas, J., Callan, J.,&Carbonell J. 2008. Document representation and query expansion models for blog recommendation. Proc. ICWSM'08.

[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]

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

Digital Library

[5]

Dugan, C., Muller, M., Millen, D.R., Geyer, W., Brownholtz, B.,&Moore, M. 2007. The dogear game: a social bookmark recommender system. Proc. GROUP'07, 387--390.

Digital Library

[6]

Geyer, W., Dugan, C., Millen, D., Muller, M.,&Freyne, J. 2008. Recommending topics for self-descriptions in online user profiles. Proc. RecSys'08, 59--66.

Digital Library

[7]

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

[8]

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

Digital Library

[9]

Guy, I., Jacovi, M., Meshulam, N., Ronen, I.,&Shahar, E. 2008. Public vs. private -- comparing public social network information with email. Proc. CSCW'08, 393--402.

Digital Library

[10]

Guy, I., Jacovi, M., Shahar, E., Meshulam, N., Soroka, V.,&Farrell, S. 2008. Harvesting with SONAR: the value aggregating social network information. Proc. CHI'08, 1017--1026.

Digital Library

[11]

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

[12]

Herlocker, J. L., Konstan, J.A.,&Riedl, J. 2000. Explaining collaborative filtering recommendations. Proc. CSCW'00, 241--250.

Digital Library

[13]

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

[14]

Jackson, A., Yates, J.,&Orlikowski, W. 2007. Corporate blogging: building community through persistent digital talk. Proc. HICSS'07, 80.

Digital Library

[15]

Java, A., Kolari, P., Finin, T., Joshi, A.,&Oates, T. 2007. Feeds that matter: a study of Bloglines subscriptions. Proc. ICWSM'07.

[16]

Kautz, H., Selman, B.,&Shah, M. 1997. ReferralWeb: Combining social networks and collaborative filtering. Commun. ACM 40, 3 (Mar. 1997) 63--65.

Digital Library

[17]

Lerman, K. 2007. Social networks and social information filtering on Digg. Proc. ICWSM'07.

[18]

McSherry, D. Explanation in recommender systems. 2005. Artif. Intell. Rev. 24, 2 (Oct. 2005), 179--197.

Digital Library

[19]

Millen, D.R., Feinberg, J.,&Kerr, B. 2006. Dogear: Social bookmarking in the enterprise. Proc. CHI'06, 111--120.

Digital Library

[20]

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

[21]

Official Facebook Blog: http://blog.facebook.com/blog.php?post=2207967130.

[22]

Official FriendFeed Blog: http://blog.friendfeed.com/2008/06/friendfeed-adds-personalized.html.

[23]

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

[24]

Seth, A.,&Zhang, J. 2008. A social network based approach to personalized recommendation of participatory media content. Proc. ICWSM'08.

[25]

Sinha, R.&Swearingen, K. 2001. Comparing recommendations made by online systems and friends. Proc. DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.

[26]

Spertus, E., Sahami, M.,&Buyukkokten, O. 2005. Evaluating similarity measures: a large-scale study in the Orkut social network. Proc. SIGKDD'05, 678--684.

Digital Library

[27]

Tepper, M. 2003. The rise of social software. netWorker 7, 3 (Sep. 2003), 18--23.

Digital Library

[28]

Tintarev, N.&Masthoff, J. 2007. A Survey of explanations in recommender systems. Proc. ICDEW'07, 801--810.

Digital Library

[29]

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

[30]

Wasserman, S.,&Faust, K. 1994. Social Network Analysis.

Cited By

Wang YMa XZhu M(2024)A knowledge graph algorithm enabled deep recommendation systemPeerJ Computer Science10.7717/peerj-cs.201010(e2010)Online publication date: 30-Jul-2024
https://doi.org/10.7717/peerj-cs.2010
Kang HLin DMartelaro NKittur AChen YHong M(2024)BioSpark: An End-to-End Generative System for Biological-Analogical Inspirations and IdeationExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651035(1-13)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651035
Gong YShang LWang D(2024)Integrating Social Explanations Into Explainable Artificial Intelligence (XAI) for Combating Misinformation: Vision and ChallengesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340423611:5(6705-6726)Online publication date: Oct-2024
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Index Terms

Personalized recommendation of social software items based on social relations
1. Human-centered computing
  1. Collaborative and social computing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

Copyright © 2009 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

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RecSys '09

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SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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

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Cited By

Wang YMa XZhu M(2024)A knowledge graph algorithm enabled deep recommendation systemPeerJ Computer Science10.7717/peerj-cs.201010(e2010)Online publication date: 30-Jul-2024
https://doi.org/10.7717/peerj-cs.2010
Kang HLin DMartelaro NKittur AChen YHong M(2024)BioSpark: An End-to-End Generative System for Biological-Analogical Inspirations and IdeationExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651035(1-13)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651035
Gong YShang LWang D(2024)Integrating Social Explanations Into Explainable Artificial Intelligence (XAI) for Combating Misinformation: Vision and ChallengesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340423611:5(6705-6726)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3404236
Xiong Y(2023)Comparative Studies on Modeling Users’ Multifaceted Interest Correlation for Social RecommendationProceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)10.2991/978-94-6463-198-2_137(1317-1328)Online publication date: 26-Jul-2023
https://doi.org/10.2991/978-94-6463-198-2_137
Wang SPaul SDe Boeck P(2023)Joint Latent Space Model for Social Networks with Multivariate AttributesPsychometrika10.1007/s11336-023-09926-588:4(1197-1227)Online publication date: 24-Aug-2023
https://doi.org/10.1007/s11336-023-09926-5
Ferreira LSilva DItzazelaia M(2023)Recommender Systems in CybersecurityKnowledge and Information Systems10.1007/s10115-023-01906-665:12(5523-5559)Online publication date: 5-Jun-2023
https://doi.org/10.1007/s10115-023-01906-6
He X(2022)Does Utilizing Online Social Relations Improve the Diversity of Personalized Recommendations?International Journal of Strategic Decision Sciences10.4018/IJSDS.30154713:1(1-15)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.4018/IJSDS.301547
Lu QGuo FZhou WWang ZJi S(2022)Mobile Social Recommendation Model Integrating Users’ Personality Traits and Relationship Strength under Privacy ConcernsSystems10.3390/systems1006019810:6(198)Online publication date: 29-Oct-2022
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Gong KSong XWang SLiu SLi YAl Hasan MXiong L(2022)ITSM-GCNProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557368(614-623)Online publication date: 17-Oct-2022
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Kang HKocielnik RHead AYang JLatzke MKittur AWeld DDowney DBragg J(2022)From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social NetworksProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517470(1-23)Online publication date: 29-Apr-2022
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