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Original Article
Intelligent Location Recommendation Based on Spatial and Market Mobility Patterns
Dr.Kamal Raj T1
Toukeer Ahmad2
Vyomdhip P3
Thanushree M4
Vignesh G5
1 Professor Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 5 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India.
Published Online: September-December 2025
Pages: 342-349
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403053
References
1. J. Park, H. J. Na, and H. Kim, “Development of a success prediction model for crowdfunding based on machine learning reflecting ESG information,” IEEE Access, vol. 12, pp. 197275–197289, 2024.
2. E. Mollick, “The dynamics of crowdfunding: An exploratory study,” Journal of Business Venturing, vol. 29, no. 1, pp. 1–16, 2014.
3. A. Ralcheva and P. Roosenboom, “Forecasting success in equity crowd- funding,” Small Business Economics, vol. 55, no. 1, pp. 39–56, 2020.
4. H. Sauermann, C. Franzoni, and K. Shafi, “Crowdfunding scientific research: Descriptive insights and correlates of funding success,” PLoS ONE, vol. 14, no. 1, e0208384, 2019.
5. C. Song, J. Luo, K. Ho¨ltta¨-Otto, W. Seering, and K. Otto, “Crowdfunding for design innovation: Prediction model with critical factors,” IEEE Transactions on Engineering Management, vol. 69, no. 4, pp. 1565– 1576, 2022.
6. J. Park, H. J. Na, and H. Kim, “A success prediction model with ESG factors for crowdfunding in Korea,” IEEE Access, vol. 12, pp. 101234– 101246, 2024.
7. J. Hobbs, G. Grigore, and M. Molesworth, “Success in the management of crowdfunding projects in the creative industries,” Internet Research, vol. 26, no. 1, pp. 146–166, 2016.
8. T. Fan, L. Gao, and Y. Steinhart, “The small predicts large effect in crowdfunding,” Journal of Consumer Research, vol. 47, no. 4, pp. 544– 565, 2020.
9. P. Zhang and M. Wang, “Supervised learning models for predictive analytics in business planning,” Journal of Machine Learning Research, vol. 25, no. 12, pp. 1–22, 2024.
10. J. Chen et al., “Text mining and natural language processing in crowd- funding: Techniques and applications,” Expert Systems with Applica- tions, vol. 198, 2024.
2. E. Mollick, “The dynamics of crowdfunding: An exploratory study,” Journal of Business Venturing, vol. 29, no. 1, pp. 1–16, 2014.
3. A. Ralcheva and P. Roosenboom, “Forecasting success in equity crowd- funding,” Small Business Economics, vol. 55, no. 1, pp. 39–56, 2020.
4. H. Sauermann, C. Franzoni, and K. Shafi, “Crowdfunding scientific research: Descriptive insights and correlates of funding success,” PLoS ONE, vol. 14, no. 1, e0208384, 2019.
5. C. Song, J. Luo, K. Ho¨ltta¨-Otto, W. Seering, and K. Otto, “Crowdfunding for design innovation: Prediction model with critical factors,” IEEE Transactions on Engineering Management, vol. 69, no. 4, pp. 1565– 1576, 2022.
6. J. Park, H. J. Na, and H. Kim, “A success prediction model with ESG factors for crowdfunding in Korea,” IEEE Access, vol. 12, pp. 101234– 101246, 2024.
7. J. Hobbs, G. Grigore, and M. Molesworth, “Success in the management of crowdfunding projects in the creative industries,” Internet Research, vol. 26, no. 1, pp. 146–166, 2016.
8. T. Fan, L. Gao, and Y. Steinhart, “The small predicts large effect in crowdfunding,” Journal of Consumer Research, vol. 47, no. 4, pp. 544– 565, 2020.
9. P. Zhang and M. Wang, “Supervised learning models for predictive analytics in business planning,” Journal of Machine Learning Research, vol. 25, no. 12, pp. 1–22, 2024.
10. J. Chen et al., “Text mining and natural language processing in crowd- funding: Techniques and applications,” Expert Systems with Applica- tions, vol. 198, 2024.
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