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Book Recommendation Based on Community Detection

  • Liu Xin
  • Haihong E
  • Junde Song
  • Meina Song
  • Junjie Tong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8351)

Abstract

In many recommendation systems, the ‘best bet’ recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommendation system is to find a few specific items which are supposed to be most appealing to the user. While providing academic library services, recommending books for readers is a significant work for constructing personal learning environment. As behaviors in social networks and internet tend to be in groups and the behavior trends are influenced much by the influential entities. In this paper, we firstly propose methods for detecting communities with similar interesting by selecting influential entities. And then propose the recommendation algorithms based on the community detection. At last, by implementation the methods in the real world dataset, our methods perform better than the traditional collaborative algorithms in precision and recall.

Keywords

Top-N Community Detection Influence recommendation 

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References

  1. 1.
    Pazzani, M.J., Billsus, D.: Content-based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  3. 3.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer (2011)Google Scholar
  4. 4.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: WWW, pp. 285–295 (2001)Google Scholar
  5. 5.
    Xiao, Y., Zhaoxin, Z., Ke, W.: Scalable Collaborative Filtering Using Incremental Update and Local Link Prediction. In: CIKM, pp. 2371–2374 (2012)Google Scholar
  6. 6.
    : Regression-based Latent Factor Models. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, pp. 19–28 (2009)Google Scholar
  7. 7.
    Ning, X., Karypis, G.: Sparse Linear Methods with Side Information for Top-N Recommendations. In: RecSys, pp. 155–162 (2012)Google Scholar
  8. 8.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Recommender Systems for Large-scale e-commerce: Scalable Neighborhood Formation Using Clustering. In: Proceedings of the 5th International Conference on Computer and Information Technology, pp. 158–167 (2002)Google Scholar
  9. 9.
    Connor, M.O., Herlocker, J.: Clustering Items for Collaborative Filtering. In: SIGIR (1999)Google Scholar
  10. 10.
    Bin, X., Jiajun, B., Chun, C., Deng, C.: An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups. In: WWW, pp. 21–30 (2012)Google Scholar
  11. 11.
    Junming, H., Xueqi, C., Jiafeng, G., Huawei, S., Kun, Y.: Social Recommendation with Interpersonal Influence. In: Proceedings of 19th European Conference on Artificial Intelligence, pp. 601–606 (2010)Google Scholar
  12. 12.
    Zike, Z., Tao, Z., Yicheng, Z.: Tag-aware Recommender Systems: a state-of-the-art Survey. Journal of Computer Science and Technology 26(5), 767–777 (2011)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Parthasarathy, S.: Data Mining at the Crossroads: Success, Failures and Learning from them. In: SIGKDD, pp. 1053–1055 (2007)Google Scholar
  14. 14.
    Alex, M.V., Jessical, L.M.W., Chitopher, W.R., Yuko, H., et al.: Systems-level Analysis of Mircobial Community Organiaztion through Combinatorial Labeling and Spectral Imaging. PNAS 108(10), 4152–4157 (2011)CrossRefGoogle Scholar
  15. 15.
    Critopher, M., Yan, X.R., Zhu, Y.J., Jean-Baptiste, R., Terran, L.: Active Learning for Node Classification in Assortative and Disassortative Networks. In: KDD, pp. 841–849 (2011)Google Scholar
  16. 16.
    Bradley, S.R., Keith, B.G.: Overlapping Community Detection by Collective Friendship Group Inference. In: ASONAM, pp. 375–379 (2010)Google Scholar
  17. 17.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)Google Scholar
  18. 18.
    Newman, M.: Modularity and Community Structure in Networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  19. 19.
    Linyuan, L., Matus, M., Chiho, Y., Yicheng, Z., et al.: Recommender Systems. Physics Reports 519, 1–49 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Liu Xin
    • 1
  • Haihong E
    • 1
  • Junde Song
    • 1
  • Meina Song
    • 1
  • Junjie Tong
    • 1
  1. 1.PCN&CAD Center LabBeijing University of Posts and TelecommunicationsChina

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