A personalized recommendation algorithm based on large-scale real micro-blog data

Abstract

With the arrival of the big data era, the amount of micro-blog users and texts is constantly increasing, and research on personalized recommendation algorithm for micro-blog texts is becoming more and more urgent. In consideration of the impact of user’s interests, trust transfer, time factor and social network, we proposed a new method for personalized recommendation. The method is based on community discovery, and recommends personalized micro-blog texts for users with the improved user model, which can use the social network of micro-blog platform effectively and optimize the utility function for micro-blog recommendation. Firstly, we used a multidimensional vector to represent the stereoscopic user model. Secondly, we proposed the improved k-means algorithm to extract the local community of users, which was also used to get the recommend micro-blog texts. Finally, the top-n micro-blog contents sorted by the effect function were recommended. We used a large number of real data to verify the algorithm proposed in this paper, and compared our method with some existing algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Rao JY, Jia AX, Feng YS, Zhao DY (2014) Ontology-based news personalized recommendation. Acta Scientiarum Naturalium Universitatis Pekinensis 50(1):1–8

    Google Scholar 

  2. 2.

    Hannon J, Bennett M, Smyth B (2010) Recommending twitter users to follow using content and collaborative filtering approaches. In: ACM conference on recommender systems. ACM, pp 199–206

  3. 3.

    Wang HY, Yang WB, Wang SC, Si-Rui L (2014) A service recommendation method based on trustworthy community. Chin J Comput 37(2):301–311

    Google Scholar 

  4. 4.

    Xiaohong Y, Feng Y (2016) Completing tags by local learning: a novel image tag completion method based on neighborhood tag vector predictor. Neural Comput Appl 27(8):1–10

    Google Scholar 

  5. 5.

    Larsson AO, Moe H (2012) Studying political microblogging: twitter users in the 2010 Swedish Election Campaign. New Media Soc 14(5):729–747

    Google Scholar 

  6. 6.

    Li F, Xu G, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27:2267. https://doi.org/10.1007/s00521-015-2060-3

    Google Scholar 

  7. 7.

    Qimin C, Qiao G, Yongliang W, Xianghua W (2014) Text clustering using VSM with feature clusters. Neural Comput Appl 26(4):995–1003

    Google Scholar 

  8. 8.

    Zhou XP, Liang X, Zhang HY (2014) SO information. User community detection on micro-blog using R-C model. J Softw 25(12):2808–2823

    Google Scholar 

  9. 9.

    Jiang SY, Mai ZK, Pang GS, Wu ML, Wang LX (2012) A survey of microblog data mining. Library Inf Serv 56(46217):136–142

    Google Scholar 

  10. 10.

    Blenn N (2012) Crawling and detecting community structure in online social networks using local information. In: Networking 2012. Springer, Berlin, p 11C25

  11. 11.

    Branting LK (2012) Context-sensitive detection of local community structure. Soc Netw Anal Min 2(3):279–289

    Google Scholar 

  12. 12.

    Hu Y, Yang B (2016) Characterizing the structure of large real networks to improve community detection. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2264-1

    Google Scholar 

  13. 13.

    Hou C, Nie F, Wang H, Yi D, Zhang C (2014) Learning highdimensional correspondence via manifold learning and local approximation. Neural Comput Appl 24(7–8):1555–1568

    Google Scholar 

  14. 14.

    Lian T, Ma J, Wang S, Cui C (2014) LDA-CF: a mixture model for collaborative filtering. J Chin Inf Process 28(02):129–135+150

  15. 15.

    Gao HM, Zhao FY (2015) A hybrid recommendation method combining collaborative filtering and content filtering. New Technol Lib Inf Serv 25906:20–26

    Google Scholar 

  16. 16.

    Zhang ZK, Zhou T, Zhang YC (2010) Personalized recommendation via integrated diffusion on userCitemCtag tripartite graphs. Physica A 389(1):179–186

    MathSciNet  Google Scholar 

  17. 17.

    Zhang F, Yuan N J, Lian D, Xie X, Ma WY (2016) Collaborative knowledge base embedding for recommender systems. In: The 22nd ACM SIGKDD international conference. ACM, pp 353–362

  18. 18.

    Ting I, Chang PS, Wang S (2012) Understanding microblog users for social recommendation based on social networks analysis. J Univ Comput Sci 18(4):554–576

    Google Scholar 

  19. 19.

    Yuan Q, Cong G, Lin CY (2014) COM: a generative model for group recommendation. In: The 20th ACM SIGKDD international conference. ACM, pp 163–172

  20. 20.

    Cai SQ, Yuan Q, Zhou P (2014) Personalized microblog recommendation model based on social network relations. J China Soc Sci Tech Inf 33(5):520–529

    Google Scholar 

  21. 21.

    Kim Y, Shim K (2011) TWITOBI: a recommendation system for twitter using probabilistic modeling. In: IEEE, international conference on data mining. IEEE Computer Society, pp 340–349

  22. 22.

    Omranpour H, Shiry Ghidary S (2016) A heuristic supervised Euclidean data difference dimension reduction for KNN classifier and its application to visual place classification. Neural Comput Appl 27:1867. https://doi.org/10.1007/s00521-015-1979-8

    Google Scholar 

  23. 23.

    Huang ZH, Zhang WJ, Tian QC, Sun LS, Xiang Y (2016) Survey on learning-to-rank based recommendation algorithms. J Softw 03:691–713

    MathSciNet  Google Scholar 

  24. 24.

    Wu S, Hofman JM, Mason WA, Watts DJ (2011) Who says what to whom on twitter. In: Proceedings of the 20th international conference on World Wide Web. Hyderabad, pp 705–714

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yangsen Zhang.

Ethics declarations

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, C., Zhang, Y. A personalized recommendation algorithm based on large-scale real micro-blog data. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05042-y

Download citation

Keywords

  • Personalized recommendation
  • K-means
  • User model
  • Word2vec