Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2786451.2786504acmconferencesArticle/Chapter ViewAbstractPublication PageswebsciConference Proceedingsconference-collections
research-article

Cross-Social Network Collaborative Recommendation

Published: 28 June 2015 Publication History

Abstract

Online social networks have become an essential part of our daily life, and an increasing number of users are using multiple online social networks simultaneously. We hypothesize that the integration of data from multiple social networks could boost the performance of recommender systems. In our study, we perform cross-social network collaborative recommendation and show that fusing multi-source data enables us to achieve higher recommendation performance as compared to various single-source baselines.

References

[1]
F. Abel, E. Herder, G.-J. Houben, N. Henze, and D. Krause. Cross-system user modeling and personalization on the social web. User Model. User-Adap. Inter., 23(2-3):169--209, 2013.
[2]
A. Farseev, N. Liqiang, M. Akbari, and T.-S. Chua. Harvesting multiple sources for user profile learning: a big data study. In ICMR, 2015.
[3]
P. Winoto and T. Tang. If you like the devil wears prada the book, will you also enjoy the devil wears prada the movie? a study of cross-domain recommendations. New Generation Computing, 26(3):209--225, 2008.
[4]
J. J.-C. Ying, E. H.-C. Lu, W.-N. Kuo, and V. S. Tseng. Urban Point-of-interest Recommendation by Mining User Check-in Behaviors. In SIGKDD Workshop on Urban Computing, 2012.

Cited By

View all
  • (2024)Cross‐network service recommendation in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.806336:13Online publication date: 18-Mar-2024
  • (2022)Group recommendation in Telegram by membership graph analyzing2022 27th International Computer Conference, Computer Society of Iran (CSICC)10.1109/CSICC55295.2022.9780527(1-7)Online publication date: 23-Feb-2022
  • (2019)A study on features of social recommender systemsArtificial Intelligence Review10.1007/s10462-019-09684-wOnline publication date: 29-Jan-2019
  • Show More Cited By

Index Terms

  1. Cross-Social Network Collaborative Recommendation

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WebSci '15: Proceedings of the ACM Web Science Conference
    June 2015
    366 pages
    ISBN:9781450336727
    DOI:10.1145/2786451
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 June 2015

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • National Research Foundation - IDM Programme Office, Republic of Singapore
    • Suomen Akatemia

    Conference

    WebSci '15
    Sponsor:
    WebSci '15: ACM Web Science Conference
    June 28 - July 1, 2015
    Oxford, United Kingdom

    Acceptance Rates

    Overall Acceptance Rate 245 of 933 submissions, 26%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)6
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 29 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Cross‐network service recommendation in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.806336:13Online publication date: 18-Mar-2024
    • (2022)Group recommendation in Telegram by membership graph analyzing2022 27th International Computer Conference, Computer Society of Iran (CSICC)10.1109/CSICC55295.2022.9780527(1-7)Online publication date: 23-Feb-2022
    • (2019)A study on features of social recommender systemsArtificial Intelligence Review10.1007/s10462-019-09684-wOnline publication date: 29-Jan-2019
    • (2019)Person, Organization, or Personage: Towards User Account Type Prediction in MicroblogsElectronic Governance and Open Society: Challenges in Eurasia10.1007/978-3-030-13283-5_9(111-122)Online publication date: 10-Feb-2019
    • (2018)SoMin.aiProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3241387(1234-1236)Online publication date: 15-Oct-2018
    • (2017)Tweet Can Be FitACM Transactions on Information Systems10.1145/308667635:4(1-34)Online publication date: 19-Aug-2017
    • (2017)Cross-Domain Recommendation via Clustering on Multi-Layer GraphsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080774(195-204)Online publication date: 7-Aug-2017
    • (2016)bBridgeProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2973836(759-761)Online publication date: 1-Oct-2016
    • (2016)"360° user profiling: past, future, and applications" by Aleksandr Farseev, Mohammad Akbari, Ivan Samborskii and Tat-Seng Chua with Martin Vesely as coordinatorACM SIGWEB Newsletter10.1145/2956573.29565772016:Summer(1-11)Online publication date: 6-Jul-2016

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media