Bayesian-inference based recommendation in online social networks

Xiwang Yang, Yang Guo, Yong Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.

Original languageEnglish (US)
Title of host publication2011 Proceedings IEEE INFOCOM
Pages551-555
Number of pages5
DOIs
StatePublished - 2011
EventIEEE INFOCOM 2011 - Shanghai, China
Duration: Apr 10 2011Apr 15 2011

Other

OtherIEEE INFOCOM 2011
CountryChina
CityShanghai
Period4/10/114/15/11

Fingerprint

Recommender systems
Bayesian networks
History

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Yang, X., Guo, Y., & Liu, Y. (2011). Bayesian-inference based recommendation in online social networks. In 2011 Proceedings IEEE INFOCOM (pp. 551-555). [5935224] https://doi.org/10.1109/INFCOM.2011.5935224

Bayesian-inference based recommendation in online social networks. / Yang, Xiwang; Guo, Yang; Liu, Yong.

2011 Proceedings IEEE INFOCOM. 2011. p. 551-555 5935224.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, X, Guo, Y & Liu, Y 2011, Bayesian-inference based recommendation in online social networks. in 2011 Proceedings IEEE INFOCOM., 5935224, pp. 551-555, IEEE INFOCOM 2011, Shanghai, China, 4/10/11. https://doi.org/10.1109/INFCOM.2011.5935224
Yang X, Guo Y, Liu Y. Bayesian-inference based recommendation in online social networks. In 2011 Proceedings IEEE INFOCOM. 2011. p. 551-555. 5935224 https://doi.org/10.1109/INFCOM.2011.5935224
Yang, Xiwang ; Guo, Yang ; Liu, Yong. / Bayesian-inference based recommendation in online social networks. 2011 Proceedings IEEE INFOCOM. 2011. pp. 551-555
@inproceedings{5a220dfff9484d34b16b6ba4f15d6f50,
title = "Bayesian-inference based recommendation in online social networks",
abstract = "In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.",
author = "Xiwang Yang and Yang Guo and Yong Liu",
year = "2011",
doi = "10.1109/INFCOM.2011.5935224",
language = "English (US)",
isbn = "9781424499212",
pages = "551--555",
booktitle = "2011 Proceedings IEEE INFOCOM",

}

TY - GEN

T1 - Bayesian-inference based recommendation in online social networks

AU - Yang, Xiwang

AU - Guo, Yang

AU - Liu, Yong

PY - 2011

Y1 - 2011

N2 - In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.

AB - In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.

UR - http://www.scopus.com/inward/record.url?scp=79960867244&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79960867244&partnerID=8YFLogxK

U2 - 10.1109/INFCOM.2011.5935224

DO - 10.1109/INFCOM.2011.5935224

M3 - Conference contribution

SN - 9781424499212

SP - 551

EP - 555

BT - 2011 Proceedings IEEE INFOCOM

ER -