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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.

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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
Zhou QZhang PGu HLu TGu N(2024)Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference PredictionACM Transactions on Information Systems10.1145/369783243:1(1-28)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3697832
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
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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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Funding Sources

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
Zhou QZhang PGu HLu TGu N(2024)Exploring Cross-Site User Modeling without Cross-Site User Identity Linkage: A Case Study of Content Preference PredictionACM Transactions on Information Systems10.1145/369783243:1(1-28)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1145/3697832
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
Zhao QChen XZhang HLi X(2024)Dynamic Hierarchical Attention Network for news recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124667255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124667
Mateos PBellogín A(2024)A systematic literature review of recent advances on context-aware recommender systemsArtificial Intelligence Review10.1007/s10462-024-10939-458:1Online publication date: 16-Nov-2024
https://doi.org/10.1007/s10462-024-10939-4
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
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