An Empirical Framework for Recommendation-based Location Services Using Deep Learning

Authors

  • V. Rohilla Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, India | Department of CSE, Maharaja Surajmal Institute of Technology, India
  • M. Kaur Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, India
  • S. Chakraborty Department of Computer Science and Technology, Lloyd Institute of Engineering and Technology, India
Volume: 12 | Issue: 5 | Pages: 9186-9191 | October 2022 | https://doi.org/10.48084/etasr.5126

Abstract

The large amount of possible online services throws a significant load on the users' service selection decision-making procedure. Α number of intelligent suggestion systems have been created in order to lower the excessive decision-making expense. Taking this into consideration, aν RLSD (Recommendation-based Location Services using Deep Learning) model is proposed in this paper. Alongside robustness, this research considers the geographic interface between the client and the service. The suggested model blends a Multi-Layer-Perceptron (MLP) with a similarity Adaptive Corrector (AC), which is meant to detect high-dimensional and non-linear connections, as well as the location correlations amongst client and services. This not only improves recommendation results but also considerably reduces difficulties due to data sparseness. As a result, the proposed RLSD has strong flexibility and is extensible when it comes to leveraging context data like location.

Keywords:

Location Services, Recommendation System, Deep Learning, Similarity Corrector

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How to Cite

[1]
V. Rohilla, M. Kaur, and S. Chakraborty, “An Empirical Framework for Recommendation-based Location Services Using Deep Learning”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 5, pp. 9186–9191, Oct. 2022.

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