Skip to main content
SpringerLink
Log in
Book cover

International Conference on Smart City and Informatization

iSCI 2019: Smart City and Informatization pp 35–48Cite as

FRFP: A Friend Recommendation Method Based on Fine-Grained Preference

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1122))

Abstract

In photography community, users are often asked and encouraged to give relevant tags based on the content of the photos when uploading them. These tags are often fine-grained and can be better used to analyze the user’s fine-grained photography preferences for friend recommendation. However, the recommendation faces challenges because the latest related research works rarely pay attention to the user’s photography preferences are fine-grained, which leads to poor friend recommendation. Therefore, we try to propose a new Friend Recommendation method by user’s Fine-grained Preference (FRFP). Firstly, FRFP method extracts the user’s fine-grained photography preference features from the perspective of the fine-grained tag. Then, we use the pagerank algorithm to calculate the importance of the preference feature tag as the score of the user-item scoring matrix, and generate a friend recommendation list through the collaborative filtering algorithm. Finally, we use user activity to weight the users in friend recommendation list, preferentially recommend users with high user activity to target user, and improve the quality of friend recommendation. The experimental results on real-word data show the effectiveness and precision of the proposed method in friend recommendation for photographers.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.facebook.com.

  2. 2.

    https://www.twitter.com.

  3. 3.

    http://mattmahoney.net/dc/enwik9.zip.

  4. 4.

    https://secure.flickr.com/.

References

  1. Mislove, A., Marcon, M., Gummadi, K., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. Association for Computing Machinery (ACM) (2007)

    Google Scholar 

  2. Badrul, S., George, K., Joseph, K., John, R.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of ACM World Wide Web Conference (2001)

    Google Scholar 

  3. Hwang, C.-L., Paul, Y.K.: Multiple attribute decision making. Methods and applications. A state-of-the-art survey (1981)

    Chapter  Google Scholar 

  4. Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)

    Article  MathSciNet  Google Scholar 

  5. Gong, N.Z.: Jointly predicting links and inferring attributes using a social-attribute network (SAN). Computer Science (2011)

    Google Scholar 

  6. Han, S., Xu, Y.: Friend recommendation of microblog in classification framework: using multiple social behavior features. In: International Conference on Behavior (2015)

    Google Scholar 

  7. Hannon, J., Bennett, M., Smyth, B.: Recommending twitter users to follow using content and collaborative filtering approaches. In: Recommender Systems, pp. 199–206 (2010)

    Google Scholar 

  8. Hasan, M.M., Shaon, N.H., Marouf, A.A., Hasan, Md.K., Khan, Md.M.: Friend recommendation framework for social networking sites using user’s online behavior. In: International Conference on Computer and Information Technology (2015)

    Google Scholar 

  9. He, C., Li, H., Xiang, F., Yong, T., Jia, Z.: A topic community-based method for friend recommendation in online social networks via joint nonnegative matrix factorization. In: Third International Conference on Advanced Cloud and Big Data (2016)

    Google Scholar 

  10. Huang, S., Zhang, J., Lu, S., Hua, X.S.: Social friend recommendation based on network correlation and feature co-clustering (2015)

    Google Scholar 

  11. Jiang, W., Jie, W., Feng, L., Wang, G., Zheng, H.: Trust evaluation in online social networks using generalized network flow. IEEE Trans. Comput. 65(3), 952–963 (2016)

    Article  MathSciNet  Google Scholar 

  12. Jiang, W., Wang, G., Bhuiyan, M.Z.A., Wu, J.: Understanding graph-based trust evaluation in online social networks: methodologies and challenges. ACM Comput. Surv. 49(1), 1–35 (2016)

    Article  Google Scholar 

  13. Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on International Conference on Machine Learning (2014)

    Google Scholar 

  14. Li, M., Jiang, W., Li, K.: Recommendation systems in real applications: algorithm and parallel architecture. In: Wang, G., Ray, I., Alcaraz Calero, J.M., Thampi, S.M. (eds.) SpaCCS 2016. LNCS, vol. 10066, pp. 45–58. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49148-6_5

    Chapter  Google Scholar 

  15. Li, Z., Fang, X., Sheng, O.: A survey of link recommendation for social networks: methods, theoretical foundations, and future research directions. Social Science Electronic Publishing (2015)

    Google Scholar 

  16. Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_28

    Chapter  Google Scholar 

  17. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of sentences and documents (2016)

    Google Scholar 

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality (2013)

    Google Scholar 

  19. Page, L., Brin, S., Motwani, R., Winograd, T.: Bringing order to the web. The pagerank citation ranking (1999)

    Google Scholar 

  20. Qiu, S., Cheng, J., Yuan, T., Leng, C., Lu, H.: Item group based pairwise preference learning for personalized ranking, pp. 1219–1222 (2014)

    Google Scholar 

  21. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Conference on Uncertainty in Artificial Intelligence (2009)

    Google Scholar 

  22. Rendle, S., Schmidt-Thieme, L.: Onlineupdating regularized kernel matrix factorization models for large-scale recommender systems (2008)

    Google Scholar 

  23. Wang, F., Li, J., Jiang, W., Wang, G.: Temporal topic-based multi-dimensional social influence evaluation in online social networks. Wirel. Pers. Commun. 95(3), 2143–2171 (2017)

    Article  Google Scholar 

  24. Wang, G., Jiang, W., Wu, J., Xiong, Z.: Fine-grained feature-based social influence evaluation in online social networks. IEEE Trans. Parall. Distrib. Syst. 25(9), 2286–2296 (2014)

    Article  Google Scholar 

  25. Wu, B.-X., Xiao, J., Chen, J.-M.: Friend recommendation by user similarity graph based on interest in social tagging systems. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS (LNAI), vol. 9227, pp. 375–386. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22053-6_41

    Chapter  Google Scholar 

  26. Xing, X.: Potential friend recommendation in online social network. In: IEEE/ACM International Conference on Green Computing and Communications and International Conference on Cyber (2010)

    Google Scholar 

  27. Xu, Y., Zeng, Q., Wang, G., Zhang, C., Ren, J., Zhang, Y.: A privacy-preserving attribute-based access control scheme, pp. 361–370 (2018)

    Google Scholar 

  28. Zheng, H., Jie, W.: Friend recommendation in online social networks: perspective of social influence maximization. In: 2017 26th International Conference on Computer Communication and Networks (ICCCN) (2017)

    Google Scholar 

Download references

Acknowledgment

This research was supported by NSFC grant 61632009 and Outstanding Young Talents Training Program in Hunan University 531118040173.

Author information

Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Mingmin Shao, Wenjun Jiang & Lei Zhang

Authors
  1. Mingmin Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjun Jiang .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. Shanghai Jiao Tong University, Shanghai, China

    Xuejia Lai

  4. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  5. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shao, M., Jiang, W., Zhang, L. (2019). FRFP: A Friend Recommendation Method Based on Fine-Grained Preference. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1301-5_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1300-8

  • Online ISBN: 978-981-15-1301-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation