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A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)


In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.


  • Recommender systems
  • Deep learning
  • Research trend analysis

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  • DOI: 10.1007/978-3-030-99619-2_10
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  1. Melville, P., Sindhwani, V.: Recommender systems. Encyl. Mach. Learn. 1, 829–838 (2010)

    Google Scholar 

  2. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52, 1–38 (2019)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  4. Laishram, A., Sahu, S.P., Padmanabhan, V., Udgata, S.K.: Collaborative filtering, matrix factorization and population based search: the nexus unveiled. In: ICONIP (2016).

  5. Amato, F., Casola, V., Mazzeo, A., Romano, S.: A semantic based methodology to classify and protect sensitive data in medical records. In: 2010 6th International Conference on Information Assurance and Security, pp. 240–246 (2010)

    Google Scholar 

  6. He, X., et al.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

    Google Scholar 

  7. Koren, Y., et al.: Matrix factorization techniques for recommender systems. IEEE Comput. 42, 30–37 (2009).

    CrossRef  Google Scholar 

  8. Zhou, Y., Wilkinson, D.M., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the Netflix prize. In: AAIM (2008).

  9. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: AutoRec: autoencoders meet collaborative filtering. In: WWW (2015).

  10. Lee, J., Kim, S., Lebanon, G., Singer, Y., Singer, Y.: Local low-rank matrix approximation. In: ICML (2013)

    Google Scholar 

  11. Salakhutdinov, R., Mnih, A., Hinton, G.E.: Restricted Boltzmann machines for collaborative filtering. In: ICML 2007 (2007).

  12. Strub, F., Mary, J.: Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: NIPS 2015 (2015)

    Google Scholar 

  13. Li, S., Kawale, J., Fu, Y.: Deep collaborative filtering via marginalized denoising auto-encoder. In: CIKM (2015).

  14. Rawat, Y.S., Kankanhalli, M.S.: ConTagNet: exploiting user context for image tag recommendation. In: ACM International Conference on Multimedia (2016).

  15. Lei, C., Liu, D., Li, W., Zha, Z.-J., Li, H.: Comparative deep learning of hybrid representations for image recommendations. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).

  16. Chen, J., et al.: Attentive collaborative filtering: multimedia recommendation with item- and component-level attention (2017).

  17. Du, X., et al.: Modeling embedding dimension correlations via convolutional neural collaborative filtering. arXiv arXiv:1906.11171 (2019)

  18. Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD (2009).

  19. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD (2008).

  20. Dai, H., Wang, Y., Trivedi, R., Song, L.: Deep coevolutionary network: embedding user and item features for recommendation. arXiv arXiv:1609.03675 (2016)

  21. Devooght, R., Bersini, H.: Long and short-term recommendations with recurrent neural networks. In: UMAP (2017).

  22. Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: SSST@EMNLP (2014).

  23. Yu, C., Zhang, C., Wang, J.: Extracting body text from academic PDF documents for text mining. arXiv arXiv:2010.12647 [cs.IR] (2020)

  24. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2001).

    CrossRef  MATH  Google Scholar 

  25. Pedregosa, F., et al.: scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  26. Van der Maaten, L., Hinton, G.E.: Visualizing data using t-SNE. J. Mach. Learn. Res. 12, 2825–2830 (2008)

    MATH  Google Scholar 

  27. Han, C., Rao, N., Sorokina, D., Subbian, K.: Scalable feature selection for (multitask) gradient boosted trees. arXiv arXiv:2109.01965 [stat.ML] (2021)

  28. Abroshan, M., Yip, K.H., Tekin, C., van der Schaar, M.: Conservative policy construction using variational autoencoders for logged data with missing values. arXiv arXiv:2109.03747 [cs.LG] (2021)

  29. Giorgio, G., Federico, N., Massimo, R.: Matrix completion of world trade. arXiv arXiv:2109.03930 [econ.G4N] (2021)

  30. Shen, Y., et al.: How powerful is graph convolution for recommendation? arXiv arXiv:2108.07567 [cs.IR] (2021)

  31. Chen, Y., et al.: Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation. arXiv arXiv:2109.02046 [cs.IR]. 12 Amato, Di Cicco, Fonisto, Giacalone (2022)

  32. Jung, S., et al.: Global-local item embedding for temporal set prediction. In: 15th ACM Conference on Recommender Systems, September 2021 (2021).

  33. Zhang, Z., Zhang, C., Niu, Z., Wang, L., Liu, Y.: GeneAnnotator: a semi-automatic annotation tool for visual scene graph. arXiv arXiv:2109.02226 [cs.CV] (2021)

  34. Zeng, Z., et al.: An evaluation-focused framework for visualization recommendation algorithms. arXiv arXiv:2109.02706 [cs.HC] (2021)

  35. Bulathwela, S., Perez-Ortiz, M., Novak, E., Yilmaz, E., Shawe-Taylor, J.: PEEK: a large dataset of learner engagement with educational videos. arXiv arXiv:2109.03154 [cs.IR] (2021)

  36. Dori-Hacohen, S., et al.: Fairness via AI: bias reduction in medical information. arXiv arXiv:2109.02202 [cs.AI] (2021)

  37. Chaney, A.J.B.: Recommendation system simulations: a discussion of two key challenges. arXiv arXiv:2109.02475 [cs.IR] (2021)

  38. Zhang, D., Wang, J.: Recommendation fairness: from static to dynamic. arXiv arXiv:2109.03150 [cs.IR] (2021)

  39. Covert, I., Lundberg, S., Lee, S.-I.: Explaining by removing: a unified framework for model explanation. arXiv arXiv:2011.14878 [cs.LG] (2020)

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This paper has been produced with the financial support of the Justice Programme of the European Union, 101046629 CREA2, JUST-2021-EJUSTICE, JUST2027 Programme. The contents of this report are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission.

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Correspondence to Mattia Fonisto .

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Amato, F., Di Cicco, F., Fonisto, M., Giacalone, M. (2022). A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham.

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