Integrating heterogeneous information via flexible regularization framework for recommendation


Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are very sparse or absent. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit this attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommender system as a heterogeneous information network and introduce meta-path-based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization-based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting users’ and items’ similarities as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impacts on users and items.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

  2. 2.

  3. 3.

  4. 4.


  1. 1.

    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  2. 2.

    BellogíN R, Cantador I, Castells P (2013) A comparative study of heterogeneous item recommendations in social systems. Inf Sci 221:142–169

    MathSciNet  Article  Google Scholar 

  3. 3.

    Burke R, Vahedian F, Mobasher B (2014) Hybrid recommendation in heterogeneous networks. In: UMAP, pp 49–60

  4. 4.

    Cantador I, Bellogin A, Vallet D (2010) Content-based recommendation in social tagging systems. In: RecSys, pp 237–240

  5. 5.

    Feng W, Wang J (2012) Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: KDD, pp 1276–1284

  6. 6.

    Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: KDD, pp 397–406

  7. 7.

    Jamali M, Lakshmanan LV (2013) Heteromf: recommendation in heterogeneous information networks using context dependent factor models. In: WWW, pp 643–653

  8. 8.

    Jones C, Ghosh J, Sharma A (2011) Learning multiple models for exploiting predictive heterogeneity in recommender systems. In: Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems, pp 17–24

  9. 9.

    Lao N, Cohen W (2010) Fast query execution for retrieval models based on path constrained random walks. In: KDD, pp 881–888

  10. 10.

    Lee H, Lee Sg (2015) Style recommendation for fashion items using heterogeneous information network. RecSys

  11. 11.

    Luo C, Pang W, Wang Z (2014) Hete-cf: social-based collaborative filtering recommendation using heterogeneous relations. In: ICDM, pp 917–922

  12. 12.

    Ma H, Yang H, Lyu MR, King I (2008) Sorec: social recommendation using probabilistic matrix factorization. In: CIKM, pp 931–940

  13. 13.

    Ma H, King I, Lyu MR (2011) Learning to recommend with social trust ensemble. In: SIGIR, pp 203–210

  14. 14.

    Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: WSDM, pp 287–296

  15. 15.

    Ma H, Zhou T, Lyu M, King I (2011) Improving recommender systems by incorporating social contextual information. ACM Trans Inf Syst 29(2):9

    Article  Google Scholar 

  16. 16.

    Shardanand U, Maes P (1995) Social information filtering: algorithms for automating word of mouth. In: Conference on human factors in computing systems

  17. 17.

    Shi C, Kong X, Huang Y, Yu PS, Wu B (2014) Hetesim: a general framework for relevance measure in heterogeneous networks. IEEE Trans Knowl Data Eng 26(10):2479–2492

    Article  Google Scholar 

  18. 18.

    Shi C, Liu J, Zhuang F, Yu PS, Wu B (2015) Integrating heterogeneous information via flexible regularization framework for recommendation. arXiv:151103759

  19. 19.

    Shi C, Zhang Z, Luo P, Yu PS, Yue Y, Wu B (2015) Semantic path based personalized recommendation on weighted heterogeneous information networks. In: CIKM, pp 453–462

  20. 20.

    Srebro N, Jaakkola T (2003) Weighted low-rank approximations. In: ICML, pp 720–727

  21. 21.

    Sun Y, Han J, Yan X, Yu P, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB, pp 992–1003

  22. 22.

    Sun Y, Norick B, Han J, Yan X, Yu PS, Yu X (2012) Integrating meta-path selection with user guided object clustering in heterogeneous information networks. In: KDD, pp 1348–1356

  23. 23.

    Vahedian F (2014) Weighted hybrid recommendation for heterogeneous network. In: RecSys, pp 429–432

  24. 24.

    Yang X, Steck H, Liu Y (2012) Circle-based recommendation in online social networks. In: KDD, pp 1267–1275

  25. 25.

    Yu X, Ren X, Gu Q, Sun Y, Han J (2013) Collaborative filtering with entity similarity regularization in heterogeneous information networks. In: IJCAI-HINA workshop

  26. 26.

    Yu X, Ren X, Sun Y, Sturt B, Khandelwal U, Gu Q, Norick B, Han J (2013) Recommendation in heterogeneous information networks with implicit user feedback. In: RecSys, pp 347–350

  27. 27.

    Yu X, Ma H, Hsu BJP, Han J (2014) On building entity recommender systems using user click log and freebase knowledge. In: WSDM, pp 263–272

  28. 28.

    Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: a heterogeneous information network approach. In: WSDM, pp 283–292

  29. 29.

    Zhang J, Tang J, Liang B, Yang Z, Wang S, Zuo J, Li J (2008) Recommendation over a heterogeneous social network. In: WAIM, pp 309–316

Download references


This work is supported by National Key Basic Research and Department (973) Program of China (No. 2013CB329606), the National Natural Science Foundation of China (No. 71231002, 61473273), the CCF-Tencent Open Fund, the Co-construction Project of Beijing Municipal Commission of Education, US NSF through grants III-1526499, and 2015 Microsoft Research Asia Collaborative Research Program.

Author information



Corresponding author

Correspondence to Fuzhen Zhuang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shi, C., Liu, J., Zhuang, F. et al. Integrating heterogeneous information via flexible regularization framework for recommendation. Knowl Inf Syst 49, 835–859 (2016).

Download citation


  • Recommender system
  • Heterogeneous information network
  • Matrix factorization
  • Similarity measure