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


  • 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 |


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.


Location Services, Recommendation System, Deep Learning, Similarity Corrector


Download data is not yet available.


Y. Wang, Z. Cai, Z.-H. Zhan, Y.-J. Gong, and X. Tong, "An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing," IEEE Transactions on Computational Social Systems, vol. 6, no. 3, pp. 414–429, Jun. 2019. DOI:

Y. Zhang, Y. Zhou, F. Wang, Z. Sun, and Q. He, "Service recommendation based on quotient space granularity analysis and covering algorithm on Spark," Knowledge-Based Systems, vol. 147, pp. 25–35, May 2018. DOI:

S. Zhang, X. Li, Z. Tan, T. Peng, and G. Wang, "A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services," Future Generation Computer Systems, vol. 94, pp. 40–50, May 2019. DOI:

S. Zhang, G. Wang, and Q. Liu, "A Dual Privacy Preserving Scheme in Continuous Location-Based Services," in IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, Aug. 2017, pp. 402–408. DOI:

L. Qi, R. Wang, C. Hu, S. Li, Q. He, and X. Xu, "Time-aware distributed service recommendation with privacy-preservation," Information Sciences, vol. 480, pp. 354–364, Apr. 2019. DOI:

W. Dou, X. Zhang, J. Liu, and J. Chen, "HireSome-II: Towards Privacy-Aware Cross-Cloud Service Composition for Big Data Applications," IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 2, pp. 455–466, Oct. 2015. DOI:

Y. Xu, L. Qi, W. Dou, and J. Yu, "Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment," Complexity, vol. 2017, Dec. 2017, Art. no. e3437854. DOI:

A. Ramlatchan, M. Yang, Q. Liu, M. Li, J. Wang, and Y. Li, "A survey of matrix completion methods for recommendation systems," Big Data Mining and Analytics, vol. 1, no. 4, pp. 308–323, Dec. 2018. DOI:

Y. Zhang et al., "Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization," IEEE Transactions on Services Computing, vol. 14, no. 5, pp. 1333–1344, Sep. 2021. DOI:

I. M. Delamer and J. L. M. Lastra, "Service-Oriented Architecture for Distributed Publish/Subscribe Middleware in Electronics Production," IEEE Transactions on Industrial Informatics, vol. 2, no. 4, pp. 281–294, Aug. 2006. DOI:

B. AlBanna, M. Sakr, S. Moussa, and I. Moawad, "Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media," ISPRS International Journal of Geo-Information, vol. 5, no. 12, Dec. 2016, Art. no. 245. DOI:

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7993–7997, Feb. 2022. DOI:

A. B. S. Salamh and H. I. Akyuz, "A Novel Feature Extraction Descriptor for Face Recognition," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8033–8038, Feb. 2022. DOI:

M. Yu, G. Fan, H. Yu, and L. Chen, "Location-based and Time-aware Service Recommendation in Mobile Edge Computing," International Journal of Parallel Programming, vol. 49, no. 5, pp. 715–731, Oct. 2021. DOI:

N. Kumar and D. Aggarwal, "LEARNING-based Focused WEB Crawler," IETE Journal of Research, pp. 1–9, Feb. 2021. DOI:

Y. Yang, Z. Zheng, X. Niu, M. Tang, Y. Lu, and X. Liao, "A Location-Based Factorization Machine Model for Web Service QoS Prediction," IEEE Transactions on Services Computing, vol. 14, no. 5, pp. 1264–1277, Sep. 2021. DOI:

J. Liu, M. Tang, Z. Zheng, X. Liu, and S. Lyu, "Location-aware and personalized collaborative filtering for web service recommendation," IEEE Transactions on Services Computing, vol. 9, no. 5, pp. 686–699, Sep. 2016. DOI:

U. Khan, K. Khan, F. Hassan, A. Siddiqui, and M. Afaq, "Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4423–4427, Aug. 2019. DOI:

L. Zhang, T. Luo, F. Zhang, and Y. Wu, "A Recommendation Model Based on Deep Neural Network," IEEE Access, vol. 6, pp. 9454–9463, 2018. DOI:

X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, "Neural Collaborative Filtering," in 26th International Conference on World Wide Web, Perth, Australia, Apr. 2017, pp. 173–182. DOI:

H.-J. Xue, X.-Y. Dai, J. Zhang, S. Huang, and J. Chen, "Deep matrix factorization models for recommender systems," in 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, Aug. 2017, pp. 3203–3209. DOI:

Z. Zheng, H. Ma, M. R. Lyu, and I. King, "QoS-Aware Web Service Recommendation by Collaborative Filtering," IEEE Transactions on Services Computing, vol. 4, no. 2, pp. 140–152, Apr. 2011. DOI:

N. Kumar, M. Gupta, D. Sharma, and I. Ofori, "Technical Job Recommendation System Using APIs and Web Crawling," Computational Intelligence and Neuroscience, vol. 2022, Jun. 2022, Art. no. e7797548. DOI:

N. Kumar, "Segmentation based Twitter Opinion Mining using Ensemble Learning," International Journal on Future Revolution in Computer Science & Communication Engineering, vol. 3, no. 9, pp. 1–9, Sep. 2017.

N. Kumar and P. Dahiya, "Weighted similarity page rank: an improvement in WPR and WSR," International Journal of Computer Engineering and Applications, vol. 11, no. 8, pp. 1–11, 2017.

J. Mor, N. Kumar, and D. Rai, "Effective presentation of results using ranking & clustering in meta search engine," COMPUSOFT, An international journal of advanced computer technology, vol. 7, no. 12, Art. no. 2957, 2018.

J. Mor, D. D. Rai, and D. N. Kumar, "An XML based Web Crawler with Page Revisit Policy and Updation in Local Repository of Search Engine," International Journal of Engineering & Technology, vol. 7, no. 3, pp. 1119–1123, Jun. 2018. DOI:

N. Kumar and P. Singh, "Meta Search Engine with Semantic Analysis and Query Processing," International Journal of Computational Intelligence Research, vol. 13, no. 8, pp. 2005–2013, 2017. DOI:

N. Kumar, "Document Clustering Approach for Meta Search Engine," IOP Conference Series: Materials Science and Engineering, vol. 225, Dec. 2017, Art. no. 012291. DOI:


How to Cite

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.


Abstract Views: 364
PDF Downloads: 162

Metrics Information
Bookmark and Share