research-article

Personalized activity streams: sifting through the "river of news"

Authors:

Ariel RavivAuthors Info & Claims

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

Pages 181 - 188

https://doi.org/10.1145/2043932.2043966

Published: 23 October 2011 Publication History

Abstract

Activity streams have emerged as a means to syndicate updates about a user or a group of users within a social network site or a set of sites. As the flood of updates becomes highly intensive and noisy, users are faced with a "needle in a haystack" challenge when they wish to read the news most interesting to them. In this work, we study activity stream personalization as a means of coping with this challenge. We experiment with an enterprise activity stream that includes status updates and news across a variety of social media applications. We examine an entity-based user profile and a stream-based profile across three dimensions: people, terms, and places, and provide a rich set of results through a user study that combines direct rating of the objects in the profile with rating of the news items it produces.

References

[1]

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

[2]

Berger, A. & Lafferty, J. 1999. Information retrieval as statistical translation. Proc. SIGIR '99, 222--229.

Digital Library

[3]

Bernstein, M. S., Suh, B., Hong, L., Chen, J., Kairam, S., & Chi, E. H. 2010. Eddi: interactive topic-based browsing of social status streams. Proc. UIST '10, 303--312.

Digital Library

[4]

Boyd, D.M. & Ellison N.B. Social network sites: Definition, history, and scholarship. J.CMC 13, 1 (Oct. 2008), 210--230.

[5]

Carmel, D., Uziel, E., Guy, I., Mass Y., & Roitman H. Folksonomy-based term extraction for word cloud generation. Proc. CIKM '11.

Digital Library

[6]

Celi, F., Di Lascio, F.M.L., Magnani, M., Pacelli, B., & Rossi, L. 2010. Social network data and practices: the case of Friendfeed. Advances in Social computing, 346--353.

Digital Library

[7]

Chen, J., Nairn, R., Nelson, L., Bernstein, M., & Chi, E. 2010. Short and tweet: experiments on recommending content from information streams. Proc. CHI '10, 1185--1194.

Digital Library

[8]

Chen, W., Chu, J., Luan, J., Bai, H., Wang, Y., & Chang, E. Y. 2009. Collaborative filtering for Orkut communities: discovery of user latent behavior. Proc. WWW '09, 681--690.

Digital Library

[9]

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

Digital Library

[10]

Freyne J., Berkovsky S., Daly E.M., and Geyer W. 2010. Social networking feeds: recommending items of interest. Proc. RecSys '10, 277--280.

Digital Library

[11]

Garcia Esparza, S., O'Mahony, M. P., & Smyth, B. 2010. On the real-time web as a source of recommendation knowledge. Proc. RecSys '10, 305--308.

Digital Library

[12]

Gupta, T., Garg, S., Carlsson, N., Mahanti, A., & Arlitt, M. 2009. Characterization of Friendfeed: A web based social aggregation service. Proc. ICWSM '09.

[13]

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

[14]

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

[15]

Guy, I., Zwerdling, N., Ronen, I., Carmel, D., & Uziel, E. 2010. Social media recommendation based on people and tags. Proc. SIGIR '10, 194--201.

Digital Library

[16]

Hannon, J., Bennett, M., & Smyth, B. 2010. Recommending twitter users to follow using content and collaborative filtering approaches. Proc. RecSys '10, 199--206.

Digital Library

[17]

Hoadley, C.M., Xu, H., Lee, J.J, & Rosson, M.B. 2010. Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry. E-Commerce Research and Applications 9, 1 (Jan. 2010), 50--60.

[18]

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

[19]

Kwak, H., Lee, C., Park, H., & Moon, S. 2010. What is Twitter, a social network or a news media?. Proc. WWW '10, 591--600.

Digital Library

[20]

Lampe, C., Ellison, N. B., & Steinfield, C. 2008. Changes in use and perception of Facebook. Proc. CSCW '08, 721--730.

Digital Library

[21]

Manning, C., Raghavan, P., & Schtze, H. 2008. Introduction to Information Retrieval. Cambridge University Press.

Digital Library

[22]

McNee, S. M., Riedl, J., & Konstan, J. A. 2006. Being accurate is not enough: how accuracy metrics have hurt recommender systems. Proc. CHI '06 Extended Abstracts, 1097--1101

Digital Library

[23]

Naaman, M., Boase, J., & Lai, C. 2010. Is it really about me?: message content in social awareness streams. Proc. CSCW '10, 189--192.

Digital Library

[24]

Official Facebook Blog: New Views for Your Home Page http://blog.facebook.com/blog.php?post=162536657130

[25]

Phelan, O., McCarthy, K., & Smyth, B. 2009. Using twitter to recommend real-time topical news. Proc. RecSys '09, 385--388.

Digital Library

[26]

Qu, M., Qiu, G., He, X., Zhang, C., Wu, H., Bu, J., & Chen, C. 2009. Probabilistic question recommendation for question answering communities. Proc. WWW '09, 1229--1230.

Digital Library

[27]

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

[28]

Sun, E., Rosenn, I., Marlow, C., & Lento, T. 2009. Gesundheit! Modeling contagion through Facebook News Feed. Proc. ICWSM.

[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

Cited By

Haugsbakken H(2019)A Research Literature Study of Enterprise SocialMedia Platforms in OrganizationsBeta10.18261/issn.1504-3134-2019-01-0633:1(84-112)Online publication date: 7-May-2019
https://doi.org/10.18261/issn.1504-3134-2019-01-06
Nouira ACheniti‐Belcadhi LBraham R(2019)An ontology‐based framework of assessment analytics for massive learningComputer Applications in Engineering Education10.1002/cae.2215527:6(1343-1360)Online publication date: 16-Aug-2019
https://doi.org/10.1002/cae.22155
Hashavit ATepper NRonen ILeiba LCohen AMitrovic TZhang JChen LChin D(2018)Implicit User Modeling in Group ChatAdjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3213586.3225236(275-280)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3213586.3225236
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Index Terms

Personalized activity streams: sifting through the "river of news"
1. 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 '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]

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Association for Computing Machinery

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Publication History

Published: 23 October 2011

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RecSys '11

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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

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Cited By

Haugsbakken H(2019)A Research Literature Study of Enterprise SocialMedia Platforms in OrganizationsBeta10.18261/issn.1504-3134-2019-01-0633:1(84-112)Online publication date: 7-May-2019
https://doi.org/10.18261/issn.1504-3134-2019-01-06
Nouira ACheniti‐Belcadhi LBraham R(2019)An ontology‐based framework of assessment analytics for massive learningComputer Applications in Engineering Education10.1002/cae.2215527:6(1343-1360)Online publication date: 16-Aug-2019
https://doi.org/10.1002/cae.22155
Hashavit ATepper NRonen ILeiba LCohen AMitrovic TZhang JChen LChin D(2018)Implicit User Modeling in Group ChatAdjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3213586.3225236(275-280)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3213586.3225236
Vo NLee KCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)The Rise of GuardiansThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210037(275-284)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210037
Zwerdling NRonen ILeiba LBarnea MMitrovic TZhang JChen LChin D(2018)Orient Me!Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209247(169-173)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209247
Guy I(2018)People Recommendation on Social MediaSocial Information Access10.1007/978-3-319-90092-6_15(570-623)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_15
Guy IPizzato LSen SGeyer WFreyne JCastells P(2016)People Recommendation TutorialProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959196(431-432)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959196
Guy IWerthner HZanker MGolbeck JSemeraro G(2015)The Role of User Location in Personalized Search and RecommendationProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2799502(236-236)Online publication date: 16-Sep-2015
https://dl.acm.org/doi/10.1145/2792838.2799502
Agarwal DChen BHe QHua ZLebanon GMa YShivaswamy PTseng HYang JZhang LCao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Personalizing LinkedIn FeedProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2788614(1651-1660)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2788614
Nepal SParis CPour PFreyne JBista S(2015)Interaction-Based Recommendations for Online CommunitiesACM Transactions on Internet Technology10.1145/277497415:2(1-21)Online publication date: 24-Jun-2015
https://dl.acm.org/doi/10.1145/2774974
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