skip to main content
10.1145/2567948.2577326acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
poster

Multi-category item recommendation using neighborhood associations in trust networks

Published: 07 April 2014 Publication History

Abstract

This paper proposes a novel recommendation method called RecDI. In the multi-category item recommendation domain, RecDI is designed to combine user ratings with information involving user's direct and indirect neighborhood associations. Through relevant benchmarking experiments on two real-world datasets, we show that RecDI achieves better performance than other traditional recommendation methods, which demonstrates the effectiveness of RecDI.

References

[1]
J. Tang, H. Gao, H. Liu, and A. D. Sarma. eTrust: Understanding trust evolution in an online world. In KDD, pages 253--261, 2012.
[2]
R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2008.

Cited By

View all
  • (2023)Knowledge Graphs: Opportunities and ChallengesArtificial Intelligence Review10.1007/s10462-023-10465-956:11(13071-13102)Online publication date: 3-Apr-2023
  • (2019)Recommendation of Item Category Based on Random Walk Model2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA.2019.8713258(226-230)Online publication date: Mar-2019
  • (2017)TruCom: Exploiting Domain-Specific Trust Networks for Multicategory Item RecommendationIEEE Systems Journal10.1109/JSYST.2015.242719311:1(295-304)Online publication date: Mar-2017
  • Show More Cited By

Index Terms

  1. Multi-category item recommendation using neighborhood associations in trust networks

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web
      April 2014
      1396 pages
      ISBN:9781450327459
      DOI:10.1145/2567948
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      • IW3C2: International World Wide Web Conference Committee

      In-Cooperation

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 April 2014

      Check for updates

      Author Tags

      1. neighborhood relations
      2. recommendation
      3. trust networks

      Qualifiers

      • Poster

      Conference

      WWW '14
      Sponsor:
      • IW3C2

      Acceptance Rates

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

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)3
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 28 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2023)Knowledge Graphs: Opportunities and ChallengesArtificial Intelligence Review10.1007/s10462-023-10465-956:11(13071-13102)Online publication date: 3-Apr-2023
      • (2019)Recommendation of Item Category Based on Random Walk Model2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA.2019.8713258(226-230)Online publication date: Mar-2019
      • (2017)TruCom: Exploiting Domain-Specific Trust Networks for Multicategory Item RecommendationIEEE Systems Journal10.1109/JSYST.2015.242719311:1(295-304)Online publication date: Mar-2017
      • (2017)Social Recommendation Terms: Probabilistic Explanation OptimizationChallenges and Opportunity with Big Data10.1007/978-3-319-61994-1_15(155-167)Online publication date: 4-Aug-2017

      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