Personalized Resource Recommendation Based on Regular Tag and User Operation

  • Sisi Liu
  • Yongjian Liu
  • Qing XieEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)


In conventional tag-based recommendation system, the sparsity and impurity of social tag data significantly increase the complexity of data processing and affect the accuracy of recommendation. To address these problems, we consider from the perspective of resource provider and propose a resource recommendation framework based on regular tags and user operation feedbacks. Based on these concepts, we design the user feature representation integrating the information of regular tags, user operations and time factor, so as to precisely discover the user preference on different tags. The personalized recommendation algorithm is designed based on collaborative filtering mechanism by analyzing the general preference modeling of different users. We conduct the experimental evaluation on a real recommendation system with extensive user and tag data. Compared with traditional user-based collaborative filtering and the social-tag-based collaborative filtering, our approach can effectively alleviate the sparsity problem of tag data and user rating data, and our proposed user feature is more accurate to improve the performance of the recommendation system.


Regular tag User operation User preference model Collaborative filtering Recommendation system 


  1. 1.
    Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: KDD Cup and Workshop in Conjunction with KDD (2009)Google Scholar
  2. 2.
    Cai, Q., Han, D.M., Li, H.S., Hu, Y.G., Chen, Y.: Personalized resource recommendation based on tags and collaborative filtering. Comput. Sci. 41(1), 69–71 (2014)Google Scholar
  3. 3.
    Chen, J.M., Sun, Y.S., Chen, M.C.: A hybrid tag-based recommendation mechanism to support prior knowledge construction. In: IEEE International Conference on Advanced Learning Technologies, pp. 23–25 (2012)Google Scholar
  4. 4.
    Cheng, Y., Qiu, G., Bu, J., Liu, K., Han, Y., Wang, C., Chen, C.: Model bloggers’ interests based on forgetting mechanism. In: International Conference on World Wide Web, pp. 1129–1130 (2008)Google Scholar
  5. 5.
    De Gemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating tags in a semantic content-based recommender. In: ACM Conference on Recommender Systems (2008)Google Scholar
  6. 6.
    Durao, F., Dolog, P.: A personalized tag-based recommendation in social web systems. In: International Workshop on Adaptation and Personalization for Web, pp. 40–49 (2012)Google Scholar
  7. 7.
    Firan, C.S., Nejdl, W., Paiu, R.: The benefit of using tag-based profiles. In: American Web Conference, pp. 32–41 (2007)Google Scholar
  8. 8.
    Jin, J., Chen, Q.: A trust-based top-k recommender system using social tagging network. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1270–1274 (2012)Google Scholar
  9. 9.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2010)CrossRefGoogle Scholar
  10. 10.
    Ma, T., Zhou, J., Tang, M., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M., Lee, S.: Social network and tag sources based augmenting collaborative recommender system. IEICE Trans. Inf. Syst. 98(4), 902–910 (2015)CrossRefGoogle Scholar
  11. 11.
    Mathes, A.: Folksonomies - cooperative classification and communication through shared matadata. Comput. Mediated Commun. 47(10), 1–13 (2004)Google Scholar
  12. 12.
    Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: International Conference on World Wide Web (2006)Google Scholar
  13. 13.
    Pan, R., Xu, G., Dolog, P.: Improving recommendations in tag-based systems with spectral clustering of tag neighbors. In: Park, J.J., Chao, H.-C., Obaidat, M.S., Kim, J. (eds.) CSA 2011 and WCC 2011. LNEE, vol. 114, pp. 355–364. Springer, Netherlands (2012)CrossRefGoogle Scholar
  14. 14.
    Sun, G., Liu, G., Zhao, L., Xu, J., Liu, A., Zhou, X.: A social trust path recommendation system in contextual online social networks. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds.) APWeb 2014. LNCS, vol. 8709, pp. 652–656. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Yuan, Z.M., Huang, C., Sun, X.Y., Li, X.X., Xu, D.R.: A microblog recommendation algorithm based on social tagging and a temporal interest evolution model. J. Zhejiang Univ. Ser. C Comput. Electron. 16(7), 532–540 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina

Personalised recommendations