NEAR: Normalized Network Embedding with Autoencoder for Top-K Item Recommendation

  • Dedong Li
  • Aimin ZhouEmail author
  • Chuan Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)


The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy of recommendation, while have ignored the diversity of the results. We find that the key to addressing this problem is to fully exploit the hidden features of the heterogeneous user-item network, and consider the impact of hot items. Accordingly, we propose a personalized top-k item recommendation method that jointly considers accuracy and diversity, which is called Normalized Network Embedding with Autoencoder for Personalized Top-K Item Recommendation, namely NEAR. Our model fully exploits the hidden features of the heterogeneous user-item network data and generates more general low dimension embedding, resulting in more accurate and diverse recommendation sequences. We compare NEAR with some state-of-the-art algorithms on the DBLP and MovieLens1M datasets, and the experimental results show that our method is able to balance the accuracy and diversity scores.


Network embedding Recommendation system Autoencoder Heterogeneous network 


  1. 1.
    Ariyoshi, Y., Kamahara, J.: A hybrid recommendation method with double SVD reduction. In: Yoshikawa, M., Meng, X., Yumoto, T., Ma, Q., Sun, L., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 6193, pp. 365–373. Springer, Heidelberg (2010). Scholar
  2. 2.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2014)CrossRefGoogle Scholar
  3. 3.
    Cai, H.Y., Zheng, V.W., Chang, C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)CrossRefGoogle Scholar
  4. 4.
    Cao, X., Shi, C., Zheng, Y., Ding, J., Li, X., Wu, B.: A heterogeneous information network method for entity set expansion in knowledge graph. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10938, pp. 288–299. Springer, Cham (2018). Scholar
  5. 5.
    Chen, C., Zheng, X., Wang, Y., Hong, F., Lin, Z.: Context-ware collaborative topic regression with social matrix factorization for recommender systems. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 9–15 (2014)Google Scholar
  6. 6.
    Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. PP(99), 1 (2018)Google Scholar
  7. 7.
    Gao, M., Chen, L., He, X., Zhou, A.: BiNE: bipartite network embedding. In: The International ACM SIGIR Conference, pp. 715–724 (2018)Google Scholar
  8. 8.
    Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: The ACM SIGKDD International Conference, pp. 305–314 (2017)Google Scholar
  9. 9.
    Luo, X., Zhou, M., Li, S., You, Z., Xia, Y., Zhu, Q.: A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 579–592 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR Workshop (2013)Google Scholar
  11. 11.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)Google Scholar
  12. 12.
    Perozzi, B., Kulkarni, V., Chen, H., Skiena, S.: Don’t walk, skip! online learning of multi-scale network embeddings, pp. 258–265 (2017)Google Scholar
  13. 13.
    Shi, C., Hu, B., Zhao, X., Yu, P.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. PP(99), 1 (2017)Google Scholar
  14. 14.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding, pp. 1067–1077 (2015)Google Scholar
  15. 15.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)Google Scholar
  16. 16.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)Google Scholar
  17. 17.
    Yang, C., Zhao, D., Zhao, D., Chang, E.Y., Chang, E.Y.: Network representation learning with rich text information. In: International Conference on Artificial Intelligence, pp. 2111–2117 (2015)Google Scholar
  18. 18.
    Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. CoRR abs/1707.07435 (2017)Google Scholar
  19. 19.
    Zhuang, F., Zhang, Z., Qian, M., Shi, C., Xie, X., He, Q.: Representation learning via dual-autoencoder for recommendation. Neural Netw. 90, 83–89 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  2. 2.School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations