article

Use of social network information to enhance collaborative filtering performance

Authors:

Hong Joo LeeAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 37, Issue 7

Pages 4772 - 4778

https://doi.org/10.1016/j.eswa.2009.12.061

Published: 01 July 2010 Publication History

Abstract

When people make decisions, they usually rely on recommendations from friends and acquaintances. Although collaborative filtering (CF), the most popular recommendation technique, utilizes similar neighbors to generate recommendations, it does not distinguish friends in a neighborhood from strangers who have similar tastes. Because social networking Web sites now make it easy to gather social network information, a study about the use of social network information in making recommendations will probably produce productive results. In this study, we developed a way to increase recommendation effectiveness by incorporating social network information into CF. We collected data about users' preference ratings and their social network relationships from a social networking Web site. Then, we evaluated CF performance with diverse neighbor groups combining groups of friends and nearest neighbors. Our results indicated that more accurate prediction algorithms can be produced by incorporating social network information into CF.

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Cited By

Mikhaylov ABhatti IDinçer HYüksel S(2024)Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spilloversComputational Economics10.1007/s10614-022-10341-863:1(305-338)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10614-022-10341-8
Zhu TLiu Y(2023)Learning personalized preferenceExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119333215:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119333
He X(2022)Does Utilizing Online Social Relations Improve the Diversity of Personalized Recommendations?International Journal of Strategic Decision Sciences10.4018/IJSDS.30154713:1(1-15)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.4018/IJSDS.301547
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Index Terms

Use of social network information to enhance collaborative filtering performance
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering

Index terms have been assigned to the content through auto-classification.

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Copyright © Elsevier Ltd © 2009.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2010

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Cited By

Mikhaylov ABhatti IDinçer HYüksel S(2024)Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spilloversComputational Economics10.1007/s10614-022-10341-863:1(305-338)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10614-022-10341-8
Zhu TLiu Y(2023)Learning personalized preferenceExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119333215:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119333
He X(2022)Does Utilizing Online Social Relations Improve the Diversity of Personalized Recommendations?International Journal of Strategic Decision Sciences10.4018/IJSDS.30154713:1(1-15)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.4018/IJSDS.301547
Ahmed MAnsari MSingh NGunjan VB. V. SKhan M(2022)Rating-Based Recommender System Based on Textual Reviews Using IoT Smart DevicesMobile Information Systems10.1155/2022/28547412022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2854741
George SLathabai HPrabhakaran TChangat M(2021)A framework for inventor collaboration recommendation system based on network approachExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114833176:COnline publication date: 15-Aug-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114833
Gasparetti FSansonetti GMicarelli A(2021)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-351:6(3975-3995)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01962-3
Goel SKumar R(2020)SoTaRePoExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.112955141:COnline publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.eswa.2019.112955
Ardissono LMauro N(2020)A compositional model of multi-faceted trust for personalized item recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.112880140:COnline publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1016/j.eswa.2019.112880
Guzun GCanahuate G(2020)High-dimensional similarity searches using query driven dynamic quantization and distributed indexingDistributed and Parallel Databases10.1007/s10619-019-07266-x38:2(255-286)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10619-019-07266-x
Roozbahani ZRezaeenour JEmamgholizadeh HJalaly Bidgoly A(2020)A systematic survey on collaborator finding systems in scientific social networksKnowledge and Information Systems10.1007/s10115-020-01483-y62:10(3837-3879)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s10115-020-01483-y
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