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Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces

Authors:

Cheng-Kang Hsieh,

Deborah EstrinAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 51 - 62

https://doi.org/10.1145/2872427.2883006

Published: 11 April 2016 Publication History

Abstract

We propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work we target personalized news and local event recommendations for their utility and societal importance. We evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition, we conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model showed significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations.

References

[1]

Mozilla, firefox interest dashboard. https://www.mozilla.org/en-US/firefox/interest-dashboard/, 2014.

[2]

Context-Aware LDA. https://github.com/changun/CA-LDA, 2015.

[3]

F. Abel, N. Henze, E. Herder, and D. Krause. Interweaving public user profiles on the web. In User Modeling, Adaptation, and Personalization, pages 16--27. Springer, 2010.

Digital Library

[4]

D. Agarwal and B.-C. Chen. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 19--28, 2009.

Digital Library

[5]

D. Agarwal and B.-C. Chen. fLDA: matrix factorization through latent dirichlet allocation. In Proceedings of the third ACM International Conference on Web Search and Data Mining, pages 91--100, 2010.

Digital Library

[6]

A. Agresti. Analysis of ordinal categorical data, volume 656. John Wiley & Sons, 2010.

[7]

E. M. Airoldi, D. M. Blei, E. A. Erosheva, and S. E. Fienberg. Handbook of Mixed Membership Models and Their Applications. CRC Press, 2014.

Digital Library

[8]

X. Amatriain, J. M. Pujol, and N. Oliver. I like it... i like it not: Evaluating user ratings noise in recommender systems. In User Modeling, Adaptation, and Personalization, pages 247--258. Springer, 2009.

Digital Library

[9]

T. Bansal, M. Das, and C. Bhattacharyya. Content driven user profiling for comment-worthy recommendations of news and blog articles. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 195--202. ACM, 2015.

Digital Library

[10]

P. Bhattacharya, M. B. Zafar, N. Ganguly, S. Ghosh, and K. P. Gummadi. Inferring user interests in the twitter social network. In Proceedings of the 8th ACM Conference on Recommender Systems, RecSys '14, pages 357--360, 2014.

Digital Library

[11]

D. M. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77--84, Apr. 2012.

Digital Library

[12]

C. Chemudugunta and P. S. M. Steyvers. Modeling general and specific aspects of documents with a probabilistic topic model. In Proceedings of the Conference on Advances in Neural Information Processing Systems, volume 19, page 241, 2007.

[13]

J. Cohen. Statistical power analysis for the behavioral sciences. Academic press, 2013.

[14]

S. Cooper. The difference between living in new york and san francisco. https://medium.com/@sarahcpr/the-difference-between-living-in-new-york-and-san-francisco-3e8ae58832a5, 2015. Accessed: 2015-04--21.

[15]

A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (methodological), pages 1--38, 1977.

[16]

D. A. Dillman, J. D. Smyth, and L. Melani. Internet, mail, and mixed-mode surveys: the tailored design method. Wiley & Sons Toronto, 2011.

Digital Library

[17]

D. Estrin. Small Data, Where N = Me. Communications of the ACM, 57(4):32--34, Apr. 2014.

Digital Library

[18]

R. Forrest and A. Kearns. Social cohesion, social capital and the neighbourhood. Urban studies, 38(12):2125--2143, 2001.

[19]

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5--53, 2004.

Digital Library

[20]

M. Hofer. Adolescents' development of individual interests: A product of multiple goal regulation? Educational Psychologist, 45(3):149--166, 2010.

[21]

B. Hogan. The presentation of self in the age of social media: Distinguishing performances and exhibitions online. Bulletin of Science, Technology & Society, 2010.

[22]

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In The Eighth IEEE International Conference on Data Mining, pages 263--272, 2008.

Digital Library

[23]

S. Iyengar and D. R. Kinder. News that matters: Television and American opinion. University of Chicago Press, 2010.

[24]

G. Keppel. Design and analysis: A researcher's handbook. Prentice-Hall, Inc, 1991.

[25]

B. Klimt and Y. Yang. The Enron corpus: A new dataset for email classification research. In Machine learning: ECML 2004, pages 217--226. Springer, 2004.

Digital Library

[26]

Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053, 2014.

[27]

R. Likert. A technique for the measurement of attitudes. Archives of psychology, 1932.

[28]

X. Lu, C. Wang, J.-M. Yang, Y. Pang, and L. Zhang. Photo2trip: generating travel routes from geo-tagged photos for trip planning. In Proceedings of the International Conference on Multimedia, pages 143--152, 2010.

Digital Library

[29]

H. Ma, H. Yang, M. R. Lyu, and I. King. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management, pages 931--940, 2008.

Digital Library

[30]

A. E. Marwick and danah boyd. I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New media & society, 13(1):114--133, 2011.

[31]

A. K. McCallum. {MALLET: A Machine Learning for Language Toolkit}. 2002.

[32]

A. Mnih and R. Salakhutdinov. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, pages 1257--1264, 2007.

Digital Library

[33]

S.-T. Park and W. Chu. Pairwise preference regression for cold-start recommendation. In Proceedings of the third ACM conference on Recommender systems, pages 21--28, 2009.

Digital Library

[34]

M. Paul and R. Girju. Cross-cultural analysis of blogs and forums with mixed-collection topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3, pages 1408--1417, 2009.

Digital Library

[35]

M. Pennacchiotti, F. Silvestri, H. Vahabi, and R. Venturini. Making your interests follow you on twitter. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pages 165--174. ACM, 2012.

Digital Library

[36]

R. D. Putnam. Bowling alone: America's declining social capital. Journal of Democracy, 6(1):65--78, 1995.

[37]

R. Řehřek and P. Sojka. Software framework for topic modelling with large corpora. 2010.

[38]

S. Robertson and H. Zaragoza. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc, 2009.

Digital Library

[39]

S. Sedhain, S. Sanner, D. Braziunas, L. Xie, and J. Christensen. Social collaborative filtering for cold-start recommendations. In Proceedings of the 8th ACM Conference on Recommender Systems, pages 345--348, 2014.

Digital Library

[40]

G. Shani and A. Gunawardana. Evaluating recommendation systems. In Recommender systems handbook, pages 257--297. Springer, 2011.

[41]

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the Sixth ACM Conference on Recommender Systems, pages 139--146, 2012.

Digital Library

[42]

Y. Shi, M. Larson, and A. Hanjalic. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys, 47(1):3, 2014.

Digital Library

[43]

R. Sinha and K. Swearingen. The role of transparency in recommender systems. In CHI '02 Extended Abstracts on Human Factors in Computing Systems, pages 830--831, 2002.

Digital Library

[44]

C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 448--456, 2011.

Digital Library

[45]

M. Weimer, A. Karatzoglou, Q. V. Le, and A. Smola. Maximum margin matrix factorization for collaborative ranking. Advances in Neural Information Processing Systems, 2007.

[46]

M. Yan, J. Sang, and C. Xu. Mining cross-network association for youtube video promotion. In Proceedings of the ACM International Conference on Multimedia, pages 557--566, 2014.

Digital Library

[47]

L. Yang, C.-K. Hsieh, and D. Estrin. Beyond classification: Latent user interests profiling from visual contents analysis. arXiv preprint arXiv:1512.06785, 2015.

Digital Library

[48]

S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: joint friendship and interest propagation in social networks. In Proceedings of the 20th International Conference on World Wide Web, pages 537--546, 2011.

Digital Library

[49]

X. Yang, Y. Li, and J. Luo. Pinterest board recommendation for twitter users. In Proceedings of the 23rd ACM International Conference on Multimedia, pages 963--966, 2015.

Digital Library

[50]

X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pages 283--292, 2014.

Digital Library

[51]

C. Zhai, A. Velivelli, and B. Yu. A cross-collection mixture model for comparative text mining. In Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 743--748, 2004.

Digital Library

[52]

Z. Zhao, Z. Cheng, L. Hong, and E. H. Chi. Improving user topic interest profiles by behavior factorization. In Proceedings of the 24th International Conference on World Wide Web, pages 1406--1416, 2015.

Digital Library

[53]

C. Zhong, D. Karamshuk, and N. Sastry. Predicting pinterest: Automating a distributed human computation. In Proceedings of the 24th International Conference on World Wide Web, pages 1417--1426, 2015.

Digital Library

Cited By

Bhattacharya SPrawesh S(2024)A review of challenges, algorithms and evaluation methods in news recommendationJournal of Information Science10.1177/01655515241244497Online publication date: 28-Apr-2024
https://doi.org/10.1177/01655515241244497
Alsayed ADam HNguyen CSpinellis DConstantinou EBacchelli A(2024)MicroRec: Leveraging Large Language Models for Microservice RecommendationProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644916(419-430)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644916
Lv PZhang QShi LGuan ZFan YLi JZhong KDeveci M(2024)Exploring on role of location in intelligent news recommendation from data analysis perspectiveInformation Sciences10.1016/j.ins.2024.120213662(120213)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120213
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Index Terms

Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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UnitedHealth Group
AOL Connected Experiences Laboratory
Google
Robert Wood Johnson Foundation
National Science Foundation
Pfizer
National Institutes of Health

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WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Bhattacharya SPrawesh S(2024)A review of challenges, algorithms and evaluation methods in news recommendationJournal of Information Science10.1177/01655515241244497Online publication date: 28-Apr-2024
https://doi.org/10.1177/01655515241244497
Alsayed ADam HNguyen CSpinellis DConstantinou EBacchelli A(2024)MicroRec: Leveraging Large Language Models for Microservice RecommendationProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644916(419-430)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644916
Lv PZhang QShi LGuan ZFan YLi JZhong KDeveci M(2024)Exploring on role of location in intelligent news recommendation from data analysis perspectiveInformation Sciences10.1016/j.ins.2024.120213662(120213)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120213
Véras DNascimento ACallou G(2023)Towards Recommender Systems Integrating Contextual Information from Multiple Domains through Tensor FactorizationArtificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications10.2174/9789815136746123010007(72-109)Online publication date: 14-Aug-2023
https://doi.org/10.2174/9789815136746123010007
Pan XSu QWei LGuo L(2023)Research on Ethical Issues and Coping Strategies of Artificial Intelligence Algorithms Recommending News with the Support of Wireless Sensing TechnologyJournal of Sensors10.1155/2023/86298492023(1-9)Online publication date: 20-Apr-2023
https://doi.org/10.1155/2023/8629849
Wu CWu FHuang YXie X(2023)Personalized News Recommendation: Methods and ChallengesACM Transactions on Information Systems10.1145/353025741:1(1-50)Online publication date: 10-Jan-2023
https://dl.acm.org/doi/10.1145/3530257
Talha MKhan HIqbal SAlghobiri MIqbal TFayyaz M(2023)Deep learning in news recommender systems: A comprehensive survey, challenges and future trendsNeurocomputing10.1016/j.neucom.2023.126881562(126881)Online publication date: Dec-2023
https://doi.org/10.1016/j.neucom.2023.126881
Lian XYuan BZhu XWang YHe YWu HSun LLyu HLiu CDong XLiao YLuo MZhang CXie JLi HChen LHuang RLin JShu CQiu XLiu ZKong DYuan LYu HYang SZhang CLiu JZhang ARangwala H(2022)Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion ParametersProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539070(3288-3298)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539070
Liu HLin HFan WRen YXu BZhang XWen DZhao NLin YYang L(2022)Self-supervised learning for fair recommender systemsApplied Soft Computing10.1016/j.asoc.2022.109126125(109126)Online publication date: Aug-2022
https://doi.org/10.1016/j.asoc.2022.109126
Smirnov ALevashova T(2022)Scenario and Architecture for Intelligent Decision Support Based on User Digital LifeArtificial Intelligence Trends in Systems10.1007/978-3-031-09076-9_38(422-433)Online publication date: 8-Jul-2022
https://doi.org/10.1007/978-3-031-09076-9_38
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