Skip to main content
SpringerLink
Log in

A Personalized Recommendation Method Considering Local and Global Influences

  • Conference paper
  • First Online:
Book cover Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10191))

Included in the following conference series:

Abstract

Social Media is one of the largest media data storage in the website. Many researchers utilize this to do some research about user interest and recommendation system. This data is like a treasure vault waiting to be utilized to develop the recommendation systems. Social common interest is one of the methodologies to implement the recommendation system among users. It performs well in community with similar interest. The drawback of it ignores the outside influence from other communities. In this paper, a methodology to calculate the global influence from outside community and to implement the recommendation system is proposed. The results could be utilized to make the recommendation system not only in local communities but also notice the outside influence of item in social media.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kaplan, A.M., Haenlein, M.: Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010)

    Article  Google Scholar 

  2. Feng, H., Qian, X.: Mining user-contributed photos for personalized product recommendation. Neurocomputing 129, 409–420 (2014)

    Article  Google Scholar 

  3. Shen, J., Cheng, Z.: Personalized video similarity measure. Multimedia Syst. 17(5), 421–433 (2011)

    Article  Google Scholar 

  4. Shen, J., Pang, H., Wang, M., Yan, S.: Modeling concept dynamics for large scale music search. In: Proceedings of the 35th International ACM SIGIR Conference on Research Development Information Retrieval, New York, NY, USA, pp. 455–464 (2012)

    Google Scholar 

  5. Qian, X., Liu, X., Zheng, C., Du, Y., Hou, X.: Tagging photos using users vocabularies. Neurocomputing 111, 144–153 (2013)

    Article  Google Scholar 

  6. Qian, X., Feng, H., Zhao, G., Mei, T.: Personalized recommendation combining user interest and social circle. IEEE Trans. Knowl. Data Eng. 26(7), 1763–1777 (2014)

    Article  Google Scholar 

  7. Li, Z., Wang, C., Yang, S., Jiang, C., Li, X.: LASS: local-activity and social-similarity based data forwarding in mobile social networks. IEEE Trans. Parallel Distrib. Syst. 26(1), 174–184 (2015)

    Article  Google Scholar 

  8. Boley, H., Bhavsar, V.C., Singh, D.A.: Agent matcher search in weighted, tree structured learning object metadata. In: Learning Objects Summit, pp. 29–30 (2004)

    Google Scholar 

  9. Rung-Ching, C., Hendry: A domain ontology in social networks for identifying user interest for personalized recommendations. J. Univ. Comput. Sci. 22(3) (2016)

    Google Scholar 

  10. Bond, R., Smith, P.B.: Culture and conformity: a meta-analysis of studies using Asch’s (1952b, 1956) line judgement task. Pshycol. Bull. 119(1), 111–137 (1996)

    Article  Google Scholar 

  11. Jiang, M.: Social contextual recommendation. In: Proceedings of the 21st ACM International on CIKM, New York, NY, USA, pp. 1267–1275 (2012)

    Google Scholar 

  12. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discover. In: Proceedings of the 17th International Conference on World Wide Web, pp. 675–684. ACM (2008)

    Google Scholar 

  13. Kwona, O., Leea, Y., Sarangibc, D.: A Galois lattice approach to a context aware privacy negotiation service. Expert Syst. Appl. 38(10), 12619–12629 (2011)

    Article  Google Scholar 

  14. Nguyen, N.P., Dinh, T.N., Tokala, S., Thai, M.T.: Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the ACM 17th Annual International Conference on Mobile Computing and Networking, pp. 85–96 (2011)

    Google Scholar 

Download references

Acknowledgment

This paper is supported by Ministry of Science and Technology, Taiwan, with project number: MOST-103-2221-E-324-028 and MOST-104-2221-E-324-019-MY2.

Author information

Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan

    Hendry, Rung-Ching Chen & Lijuan Liu

  2. College of Computer and Information Engineering, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen, 361024, Fujian, China

    Lijuan Liu

Authors
  1. Hendry
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rung-Ching Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lijuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rung-Ching Chen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology , Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology , Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hendry, Chen, RC., Liu, L. (2017). A Personalized Recommendation Method Considering Local and Global Influences. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54472-4_62

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54471-7

  • Online ISBN: 978-3-319-54472-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation