Service recommendation based on social balance theory and collaborative filtering

Lianyong Qi*, Wanchun Dou, Xuyun Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


With the increasing popularity of web service technology, many users turn to look for appropriate web services to further build their complex business applications. As an effective manner for service discovery, service recommendation technique is gaining ever-increasing attention, e.g., Collaborative Filtering (i.e., CF) recommendation. Generally, the traditional CF recommendation (e.g., user-based CF, item-based CF or hybrid CF) can achieve good recommendation results. However, due to the inherent sparsity of user-service rating data, it is possible that the target user has no similar friends and the services preferred by target user own no similar services. In this exceptional situation, traditional CF recommendation approaches cannot deliver an accurate recommendation result. In view of this shortcoming, a novel Social Balance Theory (i.e., SBT)-based service recommendation approach, i.e., RecSBT is introduced in this paper, to help improve the recommendation performance. Finally, through a set of simulation experiments deployed on MovieLens-1M dataset, we further validate the feasibility of RecSBT in terms of recommendation accuracy and recall.

Original languageEnglish
Title of host publicationService-Oriented Computing
Subtitle of host publication14th International Conference, ICSOC 2016, Proceedings
EditorsQuan Z. Sheng, Eleni Stroulia, Samir Tata, Sami Bhiri
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages9
ISBN (Print)9783319462943
Publication statusPublished - 2016
Externally publishedYes
Event14th International Conference on Service-Oriented Computing, ICSOC 2016 - Banff, Canada
Duration: 10 Oct 201613 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9936 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Service-Oriented Computing, ICSOC 2016


  • Collaborative filtering
  • Enemy user
  • Friend user
  • Service recommendation
  • Social balance theory
  • Target user


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