poster

Multi-category item recommendation using neighborhood associations in trust networks

Authors:

Zhuo YangAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 403 - 404

https://doi.org/10.1145/2567948.2577326

Published: 07 April 2014 Publication History

Get Access

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.

Digital Library

Google Scholar

[2]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2008.

Digital Library

Google Scholar

Cited By

View all

Peng CXia FNaseriparsa MOsborne F(2023)Knowledge Graphs: Opportunities and ChallengesArtificial Intelligence Review10.1007/s10462-023-10465-956:11(13071-13102)Online publication date: 3-Apr-2023
https://doi.org/10.1007/s10462-023-10465-9
Shi YJin LChen JZeng K(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
https://doi.org/10.1109/ICBDA.2019.8713258
Liu HXia FChen ZAsabere NMa JHuang R(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
https://doi.org/10.1109/JSYST.2015.2427193
Show More Cited By

Index Terms

Multi-category item recommendation using neighborhood associations in trust networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systems

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Social Trust-Aware Recommendation System: A T-Index Approach
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03

Collaborative Filtering based on similarity suffers from a variety of problems such as sparsity and scalability. In this paper, we propose an ontological model of trust between users on a social network to address the limitations of similarity measure ...
Developing trust networks based on user tagging information for recommendation making
WISE'10: Proceedings of the 11th international conference on Web information systems engineering

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its ...

Comments

Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

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.

In-Cooperation

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

Check for updates

Author Tags

Qualifiers

Poster

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
206
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Peng CXia FNaseriparsa MOsborne F(2023)Knowledge Graphs: Opportunities and ChallengesArtificial Intelligence Review10.1007/s10462-023-10465-956:11(13071-13102)Online publication date: 3-Apr-2023
https://doi.org/10.1007/s10462-023-10465-9
Shi YJin LChen JZeng K(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
https://doi.org/10.1109/ICBDA.2019.8713258
Liu HXia FChen ZAsabere NMa JHuang R(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
https://doi.org/10.1109/JSYST.2015.2427193
Liu JZhang LSheng VLaili Y(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
https://doi.org/10.1007/978-3-319-61994-1_15

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Using a trust network to improve top-N recommendation

Social Trust-Aware Recommendation System: A T-Index Approach

Developing trust networks based on user tagging information for recommendation making

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations