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A Social Balance Theory-Based Service Recommendation Approach

  • Lianyong QiEmail author
  • Xuyun Zhang
  • Yiping Wen
  • Yuming Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9464)

Abstract

With the popularity of social network, an increasing number of users attempt to find their interested web services through service recommendation, e.g., Collaborative Filtering (i.e., CF)-based service recommendation. Generally, the traditional CF-based service recommendation approaches work, when the target user owns one or more similar neighbors or friends (Neighbor and friend are interchangeable in the rest of paper) (i.e., user-based CF), or the target user’s invoked services own similar services (i.e., item-based CF). However, in certain situations, similar neighbors and similar services are absent from the user-service invocation network, which brings a great challenge for accurate service recommendation. In view of this challenge, a novel recommendation approach SBT-SR (Social Balance Theory-based Service Recommendation) is put forward in this paper. Concretely, for the target user, we first determine his/her “enemies” (antonym of “friend”, i.e., the users who have opposite preference with target user), and then look for the “potential friends” of target user, based on the “enemy’s enemy is friend” rule in Social Balance Theory. Afterwards, the services preferred by “potential friends” are recommended to the target user. Finally, through a case study and a set of experiments, we demonstrate the feasibility of our proposal.

Keywords

Service recommendation Target user Similar neighbor Similar service Dissimilar enemy Social balance theory 

Notes

Acknowledgements

This paper is supported by National Natural Science Foundation of China (No. 61402258, 61402167), China Postdoctoral Science Foundation (No. 2015M571739), Open Project of State Key Laboratory of Software Engineering (No. SKLSE2014-10-03), Open Project of State Key Lab. for Novel Software (No. KFKT2015A03), DRF (No. BSQD20110123) of QFNU.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lianyong Qi
    • 1
    • 2
    • 3
    Email author
  • Xuyun Zhang
    • 4
  • Yiping Wen
    • 5
  • Yuming Zhou
    • 1
  1. 1.Nanjing UniversityNanjingChina
  2. 2.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina
  3. 3.Qufu Normal UniversityRizhaoChina
  4. 4.Machine Learning Research GroupNICTAMelbourneAustralia
  5. 5.Hunan University of Science and TechnologyXiangtanChina

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