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Social network-based recommendation: a graph random walk kernel approach

Authors:

Xin Li,

Xin Su,

Mengyue WangAuthors Info & Claims

JCDL '12: Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries

Pages 409 - 410

https://doi.org/10.1145/2232817.2232915

Published: 10 June 2012 Publication History

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Abstract

Traditional recommender system research often explores customer, product, and transaction information in providing recommendations. Social relationships in social networks are related to individuals' preferences. This study investigates the product recommendation problem based solely on people's social network information. Taking a kernel-based approach, we capture consumer social influence similarities into a graph random walk kernel and build SVR models to predict consumer opinions. In experiments on a dataset from a movie review website, our proposed model outperforms trust-based models and state-of-the-art graph kernels.

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Cited By

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Di Sipio CDi Rocco JDi Ruscio DNguyen P(2023)MORGAN: a modeling recommender system based on graph kernelSoftware and Systems Modeling10.1007/s10270-023-01102-822:5(1427-1449)Online publication date: 4-Apr-2023
https://doi.org/10.1007/s10270-023-01102-8
Bellaachia AAl-Dhelaan M(2021)Random Walks in HypergraphInternational Journal of Education and Information Technologies10.46300/9109.2021.15.215(13-20)Online publication date: 10-Mar-2021
https://doi.org/10.46300/9109.2021.15.2
Cao KLiu YZhang H(2020)Improving the Cold Start Problem in Music Recommender SystemsJournal of Physics: Conference Series10.1088/1742-6596/1651/1/0120671651(012067)Online publication date: 26-Nov-2020
https://doi.org/10.1088/1742-6596/1651/1/012067
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Index Terms

Social network-based recommendation: a graph random walk kernel approach
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information

Published In

JCDL '12: Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries

June 2012

458 pages

ISBN:9781450311540

DOI:10.1145/2232817

General Chairs:
Karim B. Boughida
The George Washington University, USA
,
Barrie Howard
The Library of Congress, USA
,
Program Chairs:
Michael L. Nelson
Old Dominion University, USA
,
Herbert Van de Sompel
Los Alamos National Laboratory, USA
,
Ingeborg Sølvberg
Norwegian University of Science & Technology, Norway

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 June 2012

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JCDL '12

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JCDL '12: 12th ACM/IEEE-CS Joint Conference on Digital Libraries

June 10 - 14, 2012

DC, Washington, USA

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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The 2024 ACM/IEEE Joint Conference on Digital Libraries

December 16 - 20, 2024

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Cited By

View all

Di Sipio CDi Rocco JDi Ruscio DNguyen P(2023)MORGAN: a modeling recommender system based on graph kernelSoftware and Systems Modeling10.1007/s10270-023-01102-822:5(1427-1449)Online publication date: 4-Apr-2023
https://doi.org/10.1007/s10270-023-01102-8
Bellaachia AAl-Dhelaan M(2021)Random Walks in HypergraphInternational Journal of Education and Information Technologies10.46300/9109.2021.15.215(13-20)Online publication date: 10-Mar-2021
https://doi.org/10.46300/9109.2021.15.2
Cao KLiu YZhang H(2020)Improving the Cold Start Problem in Music Recommender SystemsJournal of Physics: Conference Series10.1088/1742-6596/1651/1/0120671651(012067)Online publication date: 26-Nov-2020
https://doi.org/10.1088/1742-6596/1651/1/012067
Long WSong LTian Y(2019)A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarityExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.10.008118:C(411-424)Online publication date: 15-Mar-2019
https://dl.acm.org/doi/10.1016/j.eswa.2018.10.008
Wu XZhu JXiao DLin XDing R(2018)GA-ADEMultimedia Tools and Applications10.1007/s11042-017-5162-377:3(3493-3507)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-017-5162-3
Shang SShang WShi MFeng SHong Z(2017)A Video Recommendation Algorithm Based on Hyperlink-Graph ModelInternational Journal of Software Innovation10.4018/IJSI.20170701045:3(49-63)Online publication date: Jul-2017
https://doi.org/10.4018/IJSI.2017070104
Zhang JWang CWang JYu JChen JWang C(2016)Inferring Directions of Undirected Social TiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.260508128:12(3276-3292)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1109/TKDE.2016.2605081
Shang WShang SFeng SShi M(2016)An Improved Video Recommendations Based on the Hyperlink-Graph Model2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD)10.1109/ACIT-CSII-BCD.2016.079(379-383)Online publication date: Dec-2016
https://doi.org/10.1109/ACIT-CSII-BCD.2016.079
Tian FChen YWang XLan TZheng QChao K(2015)Common Features Based Volunteer and Voluntary Activity Recommendation AlgorithmProceedings of the 2015 IEEE 12th International Conference on e-Business Engineering10.1109/ICEBE.2015.17(43-47)Online publication date: 23-Oct-2015
https://dl.acm.org/doi/10.1109/ICEBE.2015.17
Zhang JWang CWang JChung CBroder AShim KSuel T(2014)Who proposed the relationship?Proceedings of the 23rd international conference on World wide web10.1145/2566486.2567968(807-818)Online publication date: 7-Apr-2014
https://dl.acm.org/doi/10.1145/2566486.2567968
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Recommendations

Discovering unknown but interesting items on personal social network

Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks

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