Dual Similarity Regularization for Recommendation

  • Jing Zheng
  • Jian Liu
  • Chuan ShiEmail author
  • Fuzhen Zhuang
  • Jingzhi Li
  • Bin Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9652)


Recently, social recommendation becomes a hot research direction, which leverages social relations among users to alleviate data sparsity and cold-start problems in recommender systems. The social recommendation methods usually employ simple similarity information of users as social regularization on users. Unfortunately, the widely used social regularization may suffer from several aspects: (1) the similarity information of users only stems from users’ social relations; (2) it only has constraint on users; (3) it may not work well for users with low similarity. In order to overcome the shortcomings of social regularization, we propose a new dual similarity regularization to impose the constraint on users and items with high and low similarities simultaneously. With the dual similarity regularization, we design an optimization function to integrate the similarity information of users and items, and a gradient descend solution is derived to optimize the objective function. Experiments on two real datasets validate the effectiveness of the proposed solution.


Social recommendation Regularization Heterogeneous information network 



This work is supported in part by National Key Basic Research and Department (973) Program of China (No. 2013CB329606), and the National Natural Science Foundation of China (No. 71231002, 61375058, 11571161), and the CCF-Tencent Open Fund, the Co-construction Project of Beijing Municipal Commission of Education, and Shenzhen Sci.-Tech Fund No. JCYJ20140509143748226.


  1. 1.
    BellogíN, R., Cantador, I., Castells, P.: A comparative study of heterogeneous item recommendations in social systems. Inf. Sci. 221, 142–169 (2013)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cantador, I., Bellogin, A., Vallet, D.: Content-based recommendation in social tagging systems. In: RecSys, pp. 237–240 (2010)Google Scholar
  3. 3.
    Jamali, M., Lakshmanan, L.V.: Heteromf: recommendation in heterogeneous information networks using context dependent factor models. In: WWW, pp. 643–653 (2013)Google Scholar
  4. 4.
    Luo, C., Pang, W., Wang, Z.: Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations. In: ICDM, pp. 917–922 (2014)Google Scholar
  5. 5.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2011)Google Scholar
  6. 6.
    Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: Social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940 (2008)Google Scholar
  7. 7.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)Google Scholar
  8. 8.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2012)Google Scholar
  9. 9.
    Shi, C., Kong, X., Huang, Y., Yu, P.S., Wu, B.: Hetesim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)CrossRefGoogle Scholar
  10. 10.
    Srebro, N., Jaakkola, T.: Weighted low-rank approximations. In: ICML, pp. 720–727 (2003)Google Scholar
  11. 11.
    Sun, Y., Han, J., Yan, X., Yu, P., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB, pp. 992–1003 (2011)Google Scholar
  12. 12.
    Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: KDD, pp. 1267–1275 (2012)Google Scholar
  13. 13.
    Yu, X., Ren, X., Gu, Q., Sun, Y., Han, J.: Collaborative filtering with entity similarity regularization in heterogeneous information networks. In: IJCAI-HINA Workshop (2013)Google Scholar
  14. 14.
    Zhang, J., Tang, J., Liang, B., Yang, Z., Wang, S., Zuo, J., Li, J.: Recommendation over a heterogeneous social network. In: The Ninth International Conference on Web-Age Information Management, WAIM 2008, pp. 309–316. IEEE (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jing Zheng
    • 1
  • Jian Liu
    • 1
  • Chuan Shi
    • 1
    Email author
  • Fuzhen Zhuang
    • 2
  • Jingzhi Li
    • 3
  • Bin Wu
    • 1
  1. 1.Beijing Key Lab of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.Department of MathematicsSouthern University of Science and TechnologyShenzhenChina

Personalised recommendations