Preference Structure and Similarity Measure in Tag-Based Recommender Systems

  • Xi Yuan
  • Jia-jin Huang
  • Ning Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)


Social tagging systems extend recommender systems from the pair (user, item) to (user, item, tag). This paper discusses the framework of similarity measure on (user, item, tag) from qualitative and quantitative perspectives. The qualitative measure makes use of the preference structure relation on (user, item, tag), and the quantitative measure makes use of reflection on (user, item, tag). The k nearest neighbors and reverse k′ nearest neighbors are used to generate recommendations.


Recommender System Target User Collaborative Filter Mean Absolute Error Rating Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Cover, T., Hart, P.: Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)CrossRefzbMATHGoogle Scholar
  3. 3.
    Chen, J., Yin, J.: A Collaborative Filtering Recommendation Algorithm Based on Influence sets. Journal of Software 18(7), 1685–1694 (2007)CrossRefGoogle Scholar
  4. 4.
    David, G., David, N., Brain, M.O., Douglas, T.: Using Collaborative Filtering to Weave an Information Tapestry. Communication of the ACM-Special 35(12), 61–70 (1992)CrossRefGoogle Scholar
  5. 5.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  6. 6.
    Flip, K.S.M.: Influence Sets Based on Reverse Nearest Neighbor Queries. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD 2000), pp. 201–212 (2000)Google Scholar
  7. 7.
    Hofmann, T.: Probabilistic Latent Semantic Indexing. In: Proceedings of the 22nd Annual International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 50–57 (1999)Google Scholar
  8. 8.
    Huang, J.J.: Modeling Recommender Systems from Preference and Set-oriented Perspectives. In: Zhu, R., Ma, Y. (eds.) Information Engineering and Applications. LNEE, vol. 154, pp. 1068–1073. Springer, London (2012)CrossRefGoogle Scholar
  9. 9.
    Jonathan, L.H., Joseph, A.K., Loren, G.T., John, T.R.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems (TOIS) 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Kolda, T.G., Bader, B.W.: Tensor Decompositions and Applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Marko, B., Yoav, S.: Content-Based Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  12. 12.
    Rendle, S., Marinho, L.B., Nanopoulos, A., Thieme, L.S.: Learning Optimal Ranking With Tensor Factorization for Tag Recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 727–736 (2009)Google Scholar
  13. 13.
    Resnick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  14. 14.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Folk-sonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating Similarity Measures: A Large-scale Study in the Orkut Social Network. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005), pp. 678–684 (2005)Google Scholar
  16. 16.
    Symeonidis, P.: User Recommendations Based on Tensor Dimensionality Reduction. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds.) Artificial Intelligence Applications and Innovations III. IFIP AICT, vol. 296, pp. 331–340. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Valentina, Z., Licia, C.: Social Ranking: Uncovering Relevant Content Using Tag-based Recommender Systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys 2008), pp. 51–58 (2008)Google Scholar
  18. 18.
    Wong, S.K.M., Yao, Y.Y.: Preference Structure, Inference and Set-oriented Retrieval. In: Proceedings of the 14th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1991), pp. 211–218 (1991)Google Scholar
  19. 19.
    Wong, S.K.M., Cai, Y.J., Yao, Y.Y.: Computation of Term Associations by a Neural Network. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1993), pp. 107–115 (1993)Google Scholar
  20. 20.
    Yao, Y.Y., Zhong, N., Huang, J., Ou, C., Liu, C.: Using Market Value Functions for Targeted Marketing Data Mining. International Journal of Pattern Recognition and Artificial Intelligence 16(8), 1–14 (2002)CrossRefGoogle Scholar
  21. 21.
    Zhang, Z.K., Zhou, T., Zhang, Y.C.: Tag-Aware Recommender Systems: A State-of-the-Art Survey. Journal of Computer Science and Technology 26(5), 767–777 (2011)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zhang, Y.C., Blattner, M., Yu, Y.K.: Heat Conduction Process on Community Networks as a Recommendation Mode. Physical Review Letters 99(15), 154–301 (2007)CrossRefGoogle Scholar
  23. 23.
    Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite Network Projection and Personal Recommendation. Physical Review E 76(4), 046115 (2007)Google Scholar
  24. 24.
    Zhou, T., Kuscsik, Z., Liu, J.G.: Solving the Apparent Diversity-accuracy Dilemma of Recommender Systems. Proceedings of the National Academy of Sciences of the United States of America 107(10), 4511–4515 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Xi Yuan
    • 1
    • 2
  • Jia-jin Huang
    • 1
    • 2
  • Ning Zhong
    • 1
    • 2
  1. 1.International WIC InstituteBeijing University of TechnologyChina
  2. 2.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan

Personalised recommendations