Taxonomy-aware collaborative denoising autoencoder for personalized recommendation

  • Chunhong ZhangEmail author
  • Tiantian Li
  • Zhibin Ren
  • Zheng Hu
  • Yang Ji


Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.


Recommender systems Taxonomy Denoising autoencoder Collaborative filtering 



This research is supported by the National Natural Science Foundation of China under Grants No. 61602048, No. 61601046, and No. 61520106007.


  1. 1.
    Zhang Z, Xu G, Zhang P, Wang Y (2017) Personalized recommendation algorithm for social networks based on comprehensive trust. Appl Intell 47(3):659–669CrossRefGoogle Scholar
  2. 2.
    Sivapalan S, Sadeghian A, Rahnama H, Madni AM (2014) Recommender systems in e-commerce. In: 2014 world automation congress (WAC), IEEEGoogle Scholar
  3. 3.
    Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems - RecSys ’16, pp 191–198Google Scholar
  4. 4.
    Pan Q, Wang X (2017) Independent travel recommendation algorithm based on analytical hierarchy process and simulated annealing for professional tourist. Appl Intell pp 1–17Google Scholar
  5. 5.
    Koenigstein N, Dror G, Koren Y (2011) Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: Proceedings of the fifth ACM conference on recommender systems - RecSys ’11, pp 165–172Google Scholar
  6. 6.
    Mnih A, Dror G, Koren Y, Weimer M (2012) Taxonomy-informed latent factor models for implicit feedback. In: Proceedings of KDD-Cup 2011, vol 18, pp 169–181Google Scholar
  7. 7.
    Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web - WWW ’05, pp 22–32Google Scholar
  8. 8.
    Liu B, Wu Y, Gong NZ, Wu J, Xiong H, Ester M (2016) Structural analysis of user choices for mobile app recommendation. ACM Transactions on Knowledge Discovery from Data 11(2):1–23CrossRefGoogle Scholar
  9. 9.
    Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37CrossRefGoogle Scholar
  10. 10.
    Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: Proceedings of the 20th international conference on neural information processing systems - NIPS ’07), pp 1257–1264Google Scholar
  11. 11.
    Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: bayesian personalized ranking from implicit feedback. pp 452—-461Google Scholar
  12. 12.
    Yu Y, Wang C, Wang H, Gao Y (2017) Attributes coupling based matrix factorization for item recommendation. Appl Intell 46(3):521–533CrossRefGoogle Scholar
  13. 13.
    Zhang S, Yao L, Sun A (2017) Deep learning based recommender system: a survey and new perspectives. CoRR arXiv:1707.07435
  14. 14.
    Sedhain S, Menon AK, Sanner S, Xie L (2015) AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web - WWW ’15 companion, pp 111–112Google Scholar
  15. 15.
    Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: Proceedings of the 1st workshop on deep learning for recommender systems - DLRS 2016, pp 11-16Google Scholar
  16. 16.
    Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for Top-N recommender systems. In: Proceedings of the Ninth ACM international conference on web search and data mining - WSDM ’16, pp 153–162Google Scholar
  17. 17.
    Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining - KDD ’15, pp 1235–1244Google Scholar
  18. 18.
    Li S, Kawale J, Fu Y (2015) Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM ’15, pp 811–820Google Scholar
  19. 19.
    Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the Thirty-First AAAI conference on artificial intelligence (AAAI-17)Google Scholar
  20. 20.
    Menon AK, Chitrapura KP, Garg S, Agarwal D, Kota N (2011) Response prediction using collaborative filtering with hierarchies and side-information. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11, pp 141–149Google Scholar
  21. 21.
    Ahmed A, Kanagal B, Pandey S, Josifovski V, Pueyo LG, Yuan J (2013) Latent factor models with additive and hierarchically-smoothed user preferences. In: Proceedings of the sixth ACM international conference on Web search and data mining - WSDM ’13, pp 385–394Google Scholar
  22. 22.
    Kanagal B, Ahmed A, Pandey S, Josifovski V, Yuan J, Garcia-Pueyo L (2012) Supercharging recommender systems using taxonomies for learning user purchase behavior. Proceedings of the VLDB Endowment 5(10):956–967CrossRefGoogle Scholar
  23. 23.
    Zhang Y, Ahmed A, Josifovski V, Smola A (2014) Taxonomy discovery for personalized recommendation. In: Proceedings of the 7th ACM international conference on Web search and data mining - WSDM ’14, pp 243–252Google Scholar
  24. 24.
    Ren Z, Zhang C, Li T, Hu Z (2017) Taxonomy-induced matrix factorization for inferring preference of mobile telecom users. In: 2017 18th IEEE international conference on mobile data management (MDM), pp 328–331Google Scholar
  25. 25.
    Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G S, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mane D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viegas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems.
  26. 26.
    Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics (AISTATS’10), vol 9. pp 249–256Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations