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A deep learning-based hybrid model for recommendation generation and ranking

  • S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
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Abstract

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.

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Notes

  1. http://jmcauley.ucsd.edu/data/amazon/.

  2. http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

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Acknowledgements

The authors gratefully acknowledge the Science and Engineering Research Board (SERB), Department of Science and Technology, India, for the financial support through Mathematical Research Impact Centric Support (MATRICS) scheme (MTR/2019/000542). The authors also acknowledge SASTRA Deemed University, Thanjavur, for extending infrastructural support to carry out this research work.

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Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India

    N. Sivaramakrishnan, V. Subramaniyaswamy & N. Senthilselvan

  2. Departamento de Gestiòn Industrial, Agroindustrial y de Operaciones, Universidad de la Costa CUC, Barranquilla, Colombia

    Amelec Viloria

  3. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    V. Vijayakumar

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  1. N. Sivaramakrishnan
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  2. V. Subramaniyaswamy
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  3. Amelec Viloria
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  4. V. Vijayakumar
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  5. N. Senthilselvan
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Correspondence to V. Subramaniyaswamy.

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Sivaramakrishnan, N., Subramaniyaswamy, V., Viloria, A. et al. A deep learning-based hybrid model for recommendation generation and ranking. Neural Comput & Applic 33, 10719–10736 (2021). https://doi.org/10.1007/s00521-020-04844-4

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