Combining social balance theory and collaborative filtering for service recommendation in sparse environment

Lianyong Qi*, Wanchun Dou, Xuyun Zhang

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services, through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, the CF-based recommendation approaches can work well, when the target user has similar friends or the target services (i.e., the services preferred by target user) have similar services. However, in certain situations when user-service rating data is sparse, it is possible that target user has no similar friends and target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result, which brings a great challenge for accurate service recommendation. In view of this challenge, we combine Social Balance Theory (i.e., SBT) and CF to put forward a novel recommendation approach RecSBT+CF. Finally, the feasibility of our proposal is validated, through a set of simulation experiments deployed on MovieLens-1M dataset.

Original languageEnglish
Title of host publicationAdvances in Services Computing
Subtitle of host publication10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings
EditorsGuojun Wang, Yanbo Han, Gregorio Martínez Perez
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages357-374
Number of pages18
ISBN (Electronic)9783319491783
ISBN (Print)9783319491776
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event10th International Conference on Asia-Pacific Services Computing, APSCC 2016 - Zhangjiajie, China
Duration: 16 Nov 201618 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10065 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Asia-Pacific Services Computing, APSCC 2016
CountryChina
CityZhangjiajie
Period16/11/1618/11/16

Keywords

  • Collaborative filtering
  • Enemy user
  • Friend user
  • Service recommendation
  • Social balance theory
  • Sparse data

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  • Cite this

    Qi, L., Dou, W., & Zhang, X. (2016). Combining social balance theory and collaborative filtering for service recommendation in sparse environment. In G. Wang, Y. Han, & G. M. Perez (Eds.), Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings (pp. 357-374). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10065 LNCS). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-49178-3_28