Redirecting to http://dergipark.org.tr/_fragment?_hash=ncsvirKhb3jAhYU6UjWrFNfayeSH-t4ztaEGr-Zh7es&_path=id%3D1308353%26_format%3Dhtml%26_locale%3Den%26_controller%3DApp%255CController%255CSite%255CArticleController%253A%253AesiArticleStats.
Research Article
BibTex RIS Cite

Year 2024, Volume: 8 Issue: 2, 251 - 265, 31.12.2024

Ayşe Cilacı Tombuş , Ergin Eroğlu , İbrahim Halil Altun

https://doi.org/10.38088/jise.1308353

Abstract

References

  • [1] Tüsiad – Deloitte Digital. (2022). E-ticaretin Öne Çıkan Başarısı, Tüketici Davranışlarında Değişim ve Dijitalleşme. https://www.eticaretraporu.org/wp-content/uploads/2022/02/dd-tusiad-e-ticaretin- one-cikan-basarisi-tuketici-davranislarinda-degisim-ve-dijitallesme.pdf [Accessed: 9 May 2023].
  • [2] Cramer-Flood E. Insiderintelligence- eMarketer. (2023). Global Retail Ecommerce Forecast 2023 Welcome to the Slower-Growth New Normal. https://www.insiderintelligence.com/content/global- retail-ecommerce-forecast-2023 [Accessed: 15 May 2023].
  • [3] Baluch A. 38 E-Commerce Statistics Of 2023. (2023). https://www.forbes.com/advisor/business/ecommerce-statistics/ [Accessed: 2 May 2023].
  • [4] Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2018). Effects of online recommendations on consumers’ willingness to pay. Information Systems Research, 29(1), 84-102.
  • [5] Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.
  • [6] Jannach, D., & Jugovac, M. (2019). Measuring the business value of recommender systems. ACM Transactions on Management Information Systems (TMIS), 10(4), 1-23.
  • [7] Lee, D., & Hosanagar, K. (2019). How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Information Systems Research, 30(1), 239-259.
  • [8] Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2018). Effects of online recommendations on consumers’ willingness to pay. Information Systems Research, 29(1), 84-102.
  • [9] Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommendation system: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6): 734–749.
  • [10] Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159-188.
  • [11] Fleder, D. M., & Hosanagar, K. (2007). Recommender systems and their impact on sales diversity. In Proceedings of the 8th ACM conference on Electronic commerce,192-199.
  • [12] Rafeh, R. (2017). Recommender systems in ecommerce.
  • [13] Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce,158-166.
  • [14] Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a B2C E-commerce context: a review and future directions. Journal of the association for information systems, 16(2), 2.
  • [15] Bhavik Pathak , Robert Garfinkel , Ram D. Gopal , Rajkumar Venkatesan & Fang Yin (2010) Empirical Analysis of the Impact of Recommender Systems on Sales, Journal of Management Information , Systems, 27:2, 159-188.
  • [16] Senecal, S. and J. Nantel: 2004, ‘The influence of online product recommendations on consumers online choices’. Journal of Retailing 80(2), 159–169.
  • [17] Cooke, A. D., H. Sujan, M. Sujan, and B. A. Weitz: 2002, ‘Marketing the unfamiliar: the role of context and item-specific information in electronic agent recommendations’. Journal of marketing research 39(4), 488–497.
  • [18] De, P., Y. Hu, and M. S. Rahman: 2010, ‘Technology Usage and Online Sales: An Empirical Study’. Management Science 56(11), 1930–1945.
  • [19] Hinz, O. and J. Eckert: 2010, ‘The Impact of Search and Recommendation Systems on Sales in Electronic Commerce’. Bise 2(2), 67–77.
  • [20] Amatriain, X. and J. Basilico. 2012. Netflix Recommendations: Beyond the 5 stars. Retrieved from https:// medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1.
  • [21] Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., ... & Sampath, D. (2010, September). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems (pp. 293-296).
  • [22] Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. Trans. Manage. Info. Syst. 6, 4 (2015), 13:1–13:19.
  • [23] J. Katukuri, T. Könik, R. Mukherjee, and S. Kolay. 2014. Recommending similar items in large-scale online marketplaces. In Proceedings of the IEEE International Conference on Big Data 2014. 868–876.
  • [24] J . Katukuri, T. Konik, R. Mukherjee, and S. Kolay. 2015. Post-purchase recommendations in large-scale online marketplaces. In Proceedings of the IEEE International Conference on Big Data (BigData’15). 1299–1305.
  • [25] Dias, M. B., Locher, D., Li, M., El-Deredy, W., & Lisboa, P. J. (2008, October). The value of personalised recommender systems to e-business: a case study. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 291-294).
  • [26] Chen, Y., & Canny, J. F. (2011, July). Recommending ephemeral items at web scale. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 1013-1022).
  • [27] Jannach, D., & Jugovac, M. (2019). Measuring the business value of recommender systems. ACM Transactions on Management Information Systems (TMIS), 10(4), 1-23.
  • [28] Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159-188.
  • [29] Yang, Y, and Padmanabhan. (2005). B. Evaluation of online personalization systems: A survey of evaluation schemes and a knowledge-based approach. Journal of Electronic Commerce Research, 6, 2, 112-122.
  • [30] Kumar, N, and Benbasat, I. (2006). The influence of recommendations and consumer reviews on evaluations of Websites. Information Systems Research, 17, 4 425-439.
  • [31] Vaidya, N., & Khachane, A. R. (2017). Recommender systems-the need of the ecommerce ERA. International Conference on Computing Methodologies and Communication (ICCMC) 100-104. IEEE.
  • [32] Sivapalan, S., Sadeghian, A., Rahnama, H., & Madni, A. M. (2014). Recommender systems in e- commerce. In 2014 World Automation Congress (WAC) 179-184. IEEE.

Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis

Year 2024, Volume: 8 Issue: 2, 251 - 265, 31.12.2024

Ayşe Cilacı Tombuş , Ergin Eroğlu , İbrahim Halil Altun

https://doi.org/10.38088/jise.1308353

Abstract

Recommender systems in the industrial sector are experiencing a growing application within e-commerce platforms, focusing on tailoring customer shopping experiences. This trend has led to increased customer satisfaction and enhanced sales outcomes for businesses operating in this domain. Despite the widespread prevalence of e-commerce globally, there exists a noticeable gap in the empirical assessment of recommender system performance for business objectives, particularly in the context of utilizing data mining methodologies and big data analytics.
This research aims to address this gap by scrutinizing authentic global e-commerce data that spans diverse countries, industries, and scales. The primary objective is to ascertain the impact of recommender systems, measured in terms of contribution rate, click-through rate, conversion rate, and revenue, by leveraging advanced big data analytics and data mining techniques. The study utilizes average values derived from an extensive dataset comprising 200 distinct e-commerce websites, representing a spectrum of 25 countries distributed across five different regions. Notably, this research represents a pioneering initiative in the literature as it harnesses and analyzes empirical data on such a comprehensive scale derived from various global e-commerce platforms.

Keywords

data mining data analysis e-commerce recommender systems big data

Supporting Institution

Segmentify Yazılım A.Ş.

Thanks

We thank to Segmentify Yazılım A.Ş. for their support.

References

  • [1] Tüsiad – Deloitte Digital. (2022). E-ticaretin Öne Çıkan Başarısı, Tüketici Davranışlarında Değişim ve Dijitalleşme. https://www.eticaretraporu.org/wp-content/uploads/2022/02/dd-tusiad-e-ticaretin- one-cikan-basarisi-tuketici-davranislarinda-degisim-ve-dijitallesme.pdf [Accessed: 9 May 2023].
  • [2] Cramer-Flood E. Insiderintelligence- eMarketer. (2023). Global Retail Ecommerce Forecast 2023 Welcome to the Slower-Growth New Normal. https://www.insiderintelligence.com/content/global- retail-ecommerce-forecast-2023 [Accessed: 15 May 2023].
  • [3] Baluch A. 38 E-Commerce Statistics Of 2023. (2023). https://www.forbes.com/advisor/business/ecommerce-statistics/ [Accessed: 2 May 2023].
  • [4] Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2018). Effects of online recommendations on consumers’ willingness to pay. Information Systems Research, 29(1), 84-102.
  • [5] Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19.
  • [6] Jannach, D., & Jugovac, M. (2019). Measuring the business value of recommender systems. ACM Transactions on Management Information Systems (TMIS), 10(4), 1-23.
  • [7] Lee, D., & Hosanagar, K. (2019). How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Information Systems Research, 30(1), 239-259.
  • [8] Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2018). Effects of online recommendations on consumers’ willingness to pay. Information Systems Research, 29(1), 84-102.
  • [9] Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommendation system: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6): 734–749.
  • [10] Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159-188.
  • [11] Fleder, D. M., & Hosanagar, K. (2007). Recommender systems and their impact on sales diversity. In Proceedings of the 8th ACM conference on Electronic commerce,192-199.
  • [12] Rafeh, R. (2017). Recommender systems in ecommerce.
  • [13] Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce,158-166.
  • [14] Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a B2C E-commerce context: a review and future directions. Journal of the association for information systems, 16(2), 2.
  • [15] Bhavik Pathak , Robert Garfinkel , Ram D. Gopal , Rajkumar Venkatesan & Fang Yin (2010) Empirical Analysis of the Impact of Recommender Systems on Sales, Journal of Management Information , Systems, 27:2, 159-188.
  • [16] Senecal, S. and J. Nantel: 2004, ‘The influence of online product recommendations on consumers online choices’. Journal of Retailing 80(2), 159–169.
  • [17] Cooke, A. D., H. Sujan, M. Sujan, and B. A. Weitz: 2002, ‘Marketing the unfamiliar: the role of context and item-specific information in electronic agent recommendations’. Journal of marketing research 39(4), 488–497.
  • [18] De, P., Y. Hu, and M. S. Rahman: 2010, ‘Technology Usage and Online Sales: An Empirical Study’. Management Science 56(11), 1930–1945.
  • [19] Hinz, O. and J. Eckert: 2010, ‘The Impact of Search and Recommendation Systems on Sales in Electronic Commerce’. Bise 2(2), 67–77.
  • [20] Amatriain, X. and J. Basilico. 2012. Netflix Recommendations: Beyond the 5 stars. Retrieved from https:// medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1.
  • [21] Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., ... & Sampath, D. (2010, September). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems (pp. 293-296).
  • [22] Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. Trans. Manage. Info. Syst. 6, 4 (2015), 13:1–13:19.
  • [23] J. Katukuri, T. Könik, R. Mukherjee, and S. Kolay. 2014. Recommending similar items in large-scale online marketplaces. In Proceedings of the IEEE International Conference on Big Data 2014. 868–876.
  • [24] J . Katukuri, T. Konik, R. Mukherjee, and S. Kolay. 2015. Post-purchase recommendations in large-scale online marketplaces. In Proceedings of the IEEE International Conference on Big Data (BigData’15). 1299–1305.
  • [25] Dias, M. B., Locher, D., Li, M., El-Deredy, W., & Lisboa, P. J. (2008, October). The value of personalised recommender systems to e-business: a case study. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 291-294).
  • [26] Chen, Y., & Canny, J. F. (2011, July). Recommending ephemeral items at web scale. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 1013-1022).
  • [27] Jannach, D., & Jugovac, M. (2019). Measuring the business value of recommender systems. ACM Transactions on Management Information Systems (TMIS), 10(4), 1-23.
  • [28] Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159-188.
  • [29] Yang, Y, and Padmanabhan. (2005). B. Evaluation of online personalization systems: A survey of evaluation schemes and a knowledge-based approach. Journal of Electronic Commerce Research, 6, 2, 112-122.
  • [30] Kumar, N, and Benbasat, I. (2006). The influence of recommendations and consumer reviews on evaluations of Websites. Information Systems Research, 17, 4 425-439.
  • [31] Vaidya, N., & Khachane, A. R. (2017). Recommender systems-the need of the ecommerce ERA. International Conference on Computing Methodologies and Communication (ICCMC) 100-104. IEEE.
  • [32] Sivapalan, S., Sadeghian, A., Rahnama, H., & Madni, A. M. (2014). Recommender systems in e- commerce. In 2014 World Automation Congress (WAC) 179-184. IEEE.
There are 32 citations in total.

Details

Primary Language English
Subjects Software Engineering, Industrial Engineering
Journal Section Research Articles
Authors

Ayşe Cilacı Tombuş 0000-0002-0556-7482

Ergin Eroğlu 0009-0002-0627-4048

İbrahim Halil Altun 0009-0002-0310-7930

Early Pub Date December 16, 2024
Publication Date December 31, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Cilacı Tombuş, A., Eroğlu, E., & Altun, İ. H. (2024). Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis. Journal of Innovative Science and Engineering, 8(2), 251-265. https://doi.org/10.38088/jise.1308353
AMA Cilacı Tombuş A, Eroğlu E, Altun İH. Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis. JISE. December 2024;8(2):251-265. doi:10.38088/jise.1308353
Chicago Cilacı Tombuş, Ayşe, Ergin Eroğlu, and İbrahim Halil Altun. “Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis”. Journal of Innovative Science and Engineering 8, no. 2 (December 2024): 251-65. https://doi.org/10.38088/jise.1308353.
EndNote Cilacı Tombuş A, Eroğlu E, Altun İH (December 1, 2024) Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis. Journal of Innovative Science and Engineering 8 2 251–265.
IEEE A. Cilacı Tombuş, E. Eroğlu, and İ. H. Altun, “Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis”, JISE, vol. 8, no. 2, pp. 251–265, 2024, doi: 10.38088/jise.1308353.
ISNAD Cilacı Tombuş, Ayşe et al. “Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis”. Journal of Innovative Science and Engineering 8/2 (December2024), 251-265. https://doi.org/10.38088/jise.1308353.
JAMA Cilacı Tombuş A, Eroğlu E, Altun İH. Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis. JISE. 2024;8:251–265.
MLA Cilacı Tombuş, Ayşe et al. “Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis”. Journal of Innovative Science and Engineering, vol. 8, no. 2, 2024, pp. 251-65, doi:10.38088/jise.1308353.
Vancouver Cilacı Tombuş A, Eroğlu E, Altun İH. Impact Of Recommender Systems in E-Commerce – A Worldwide Empirical Analysis. JISE. 2024;8(2):251-65.
 Download Cover Image


Creative Commons License

The works published in Journal of Innovative Science and Engineering (JISE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.