Enhancing Collaborative Filtering with Multi-label Classification

  • Yang ZhouEmail author
  • Ling Liu
  • Qi Zhang
  • Kisung Lee
  • Balaji Palanisamy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)


This paper presents a multi-label classification based CF framework, MLCF, which improves the quality of recommendation in the presence of data sparsity by learning over a heterogeneous information network consisting of a rating bipartite graph, a user graph and an item graph. MLCF is novel by three unique features. First, we explore the latent correlations among users and items w.r.t. a given set of K semantic categories beyond user-item ratings by employing multi-label clustering of items, and multi-label classification of users and rating-based similarities on the heterogeneous network. Second, based on the user/item/similarity multi-label clustering/classification, we propose a fine-grained multi-label classification based rating similarity measure to capture the class-specific relationships between users by introducing a novel concept of vertex-edge homophily. Third but not the least, we propose to integrate two kinds of multi-label classification based CF models focusing on rating and social information into a unified prediction model.


  1. 1.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. In: VLDB, pp. 718–729 (2009)CrossRefGoogle Scholar
  2. 2.
    Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema. In: KDD (2009)Google Scholar
  3. 3.
    Zhou, Y., Cheng, H., Yu, J.X.: Clustering large attributed graphs: an efficient incremental approach. In: ICDM, pp. 689–698 (2010)Google Scholar
  4. 4.
    Sun, Y., Aggarwal, C.C., Han, J.: Relation strength-aware clustering of heterogeneous information networks with incomplete attributes. PVLDB 5(5), 394–405 (2012)Google Scholar
  5. 5.
    Kong, X., Yu, P.S., Ding, Y., Wild, D.J.: Meta path-based collective classification in heterogeneous information networks. In: CIKM (2012)Google Scholar
  6. 6.
    Gu, Q., Aggarwal, C., Liu, J., Han, J.: Selective sampling on graphs for classification. In: KDD, pp. 131–139 (2013)Google Scholar
  7. 7.
    Zhou, Y., Liu, L.: Social influence based clustering of heterogeneous information networks. In: KDD, pp. 338–346 (2013)Google Scholar
  8. 8.
    Zhou, Y., Liu, L.: Activity-edge centric multi-label classification for mining heterogeneous information networks, In: KDD, pp. 1276–1285 (2014)Google Scholar
  9. 9.
    Zhou, Y., Liu, L., Lee, K., Pu, C., Zhang, Q.: Fast iterative graph computation with resource aware graph parallel abstractions. In: HPDC, pp. 179–190 (2015)Google Scholar
  10. 10.
    Zhou, Y., Liu, L.: Social influence based clustering and optimization over heterogeneous information networks. TKDD 10, 1–53 (2015)CrossRefGoogle Scholar
  11. 11.
    Yang, P., Zhao, P., Zheng, V.W., Li, X.: An aggressive graph-based selective sampling algorithm for classification. In: ICDM (2015)Google Scholar
  12. 12.
    Zhou, Y., Liu, L., Buttler, D.: Integrating vertex-centric clustering with edge-centric clustering for meta path graph analysis. In: KDD, pp. 1563–1572 (2015)Google Scholar
  13. 13.
    Zhou, Y., Liu, L., Lee, K., Zhang, Q.: Graphtwist: fast iterative graph computation with two-tier optimizations. In: PVLDB, vol. 8, no. 11, pp. 1262–1273 (2015)CrossRefGoogle Scholar
  14. 14.
    Lee, K., et al.: Scaling iterative graph computations with GraphMap. In: PVLDB, vol. 8, no. 11, pp. 1262–1273 (2015)Google Scholar
  15. 15.
    Yu, W., Cheng, W., Aggarwal, C.C., Chen, H., Wang, W.: Link prediction with spatial and temporal consistency in dynamic networks. In: IJCAI, pp. 3343–3349 (2017)Google Scholar
  16. 16.
    Zhou, Y., et al.: Density-adaptive local edge representation learning with generative adversarial network multi-label edge classification. In: ICDM (2018)Google Scholar
  17. 17.
    Zhou, Y., Amimeur, A., Jiang, C., Dou, D., Jin, R., Wang, P.: Density-aware local Siamese autoencoder network embedding with autoencoder graph clustering. In: BigData (2018)Google Scholar
  18. 18.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW (1994)Google Scholar
  19. 19.
    Herlocker, J.L., Konstan, J.A., Borchers, J.R.A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230–237 (1999)Google Scholar
  20. 20.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 158–167 (2001)Google Scholar
  21. 21.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. In: TOIS, vol. 22, pp. 143–177 (2004)CrossRefGoogle Scholar
  22. 22.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of dimensionality reduction in recommender system - a case study. In: WEBKDD (2000)Google Scholar
  23. 23.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS (2000)Google Scholar
  24. 24.
    Yu, K., Zhu, S., Lafferty, J., Gong, Y.: Fast nonparametric matrix factorization for largescale collaborative filtering. In: SIGIR, pp. 211–218 (2009)Google Scholar
  25. 25.
    Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: SIGIR, pp. 661–670 (2012)Google Scholar
  26. 26.
    Lee, J., Bengio, S., Kim, S., Lebanon, G., Singer, Y.: Local collaborative ranking. In: WWW (2014)Google Scholar
  27. 27.
    Zhang, M., Tang, J., Zhang, X., Xue, X.: Addressing cold start in recommender systems: a semi-supervised co-training algorithm. In: SIGIR, pp. 73–82 (2014)Google Scholar
  28. 28.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD (2008)Google Scholar
  29. 29.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: ICCIT (2002)Google Scholar
  30. 30.
    O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: SIGIR (1999)Google Scholar
  31. 31.
    George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: ICDM (2005)Google Scholar
  32. 32.
    Xu, B., Bu, J., Chen, C., Cai, D.: An exploration of improving collaborative recommender systems via user-item subgroups. In: WWW, pp. 21–30 (2012)Google Scholar
  33. 33.
    Zhang, Y., Zhang, M., Liu, Y., Ma, S.: Improve collaborative filtering through bordered block diagonal form matrices. In: SIGIR, pp. 313–322 (2013)Google Scholar
  34. 34.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940 (2008)Google Scholar
  35. 35.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR (2009)Google Scholar
  36. 36.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp. 135–142 (2010)Google Scholar
  37. 37.
    Noel, J., et al.: New objective functions for social collaborative filtering. In: WWW, pp. 859–868 (2012)Google Scholar
  38. 38.
    Liu, X., Aberer, K.: SoCo: a social network aided context-aware recommender system. In: WWW (2013)Google Scholar
  39. 39.
    Ma, H.: An experimental study on implicit social recommendation. In: SIGIR, pp. 73–82 (2013)Google Scholar
  40. 40.
    Zhang, C, Yu, L, Wang, Y., Shah, C., Zhang, X.: Collaborative user network embedding for social recommender systems. In: SDM, pp. 355–366 (2017)Google Scholar
  41. 41.
    Xu, L., Jiang, C., Chen, Y., Ren, Y., Liu, K.: User participation in collaborative filtering-based recommendation systems: a game theoretic approach. IEEE Trans. Cybern. vol. 49, no. 4, pp. 1339–1352 (2019)CrossRefGoogle Scholar
  42. 42.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: KDD, pp. 397–406 (2009)Google Scholar
  43. 43.
    Jamali, M., Ester, M.: Using a trust network to improve top-n recommendation. In: RecSys (2009)Google Scholar
  44. 44.
    Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. ESWA 69, 29–39 (2017)Google Scholar
  45. 45.
    Shmueli, E., Kagian, A., Koren, Y., Lempel, R.: Care to comment? recommendations for commenting on news stories. In: WWW, pp. 429–438 (2012)Google Scholar
  46. 46.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley, Boston (2005)Google Scholar
  47. 47.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefGoogle Scholar
  48. 48.
    Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research. Mcgraw-Hill College, New York (1995)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yang Zhou
    • 1
    Email author
  • Ling Liu
    • 2
  • Qi Zhang
    • 3
  • Kisung Lee
    • 4
  • Balaji Palanisamy
    • 5
  1. 1.Auburn UniversityAuburnUSA
  2. 2.Georgia Institute of TechnologyAtlantaUSA
  3. 3.IBM T.J. Watson Research CenterYorktown HeightsUSA
  4. 4.Louisiana State UniversityBaton RougeUSA
  5. 5.University of PittsburghPittsburghUSA

Personalised recommendations