Skip to main content
SpringerLink
Log in

Artificial Intelligence in E-Commerce

  • Chapter
  • First Online:
The Future of E-commerce

  • 142 Accesses

Abstract

This chapter discusses the growing importance of artificial intelligence in e-commerce, starting with the challenge of defining AI itself. It identifies and discusses several key areas of e-commerce where AI is playing and will continue to play an increasing role, namely fulfilment, inventory control, chatbots and avatars, personalisation and recommendation, automated order systems, and AI-driven ads. The significant role of AI in SEO and content creation was also discussed, where tools such as ChatGPT can automate and optimise e-commerce content. However, challenges such as the problem of hallucination of LLMs, quality, and originality of content are noted. The final section of the chapter shows the results of an experiment using ChatGPT for SEO, demonstrating the results and potential for improving search engine rankings for e-commerce sites using such tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Alamdari, P. M., Navimipour, N. J., Hosseinzadeh, M., Safaei, A. A., & Darwesh, A. (2020). A systematic study on the recommender systems in the E-commerce. IEEE Access, 8, 115694–115716.

    Article  Google Scholar 

  • Allal-Chérif, O., Simón-Moya, V., & Ballester, A. C. C. (2021). Intelligent purchasing: How artificial intelligence can redefine the purchasing function. Journal of Business Research, 124, 69–76.

    Article  Google Scholar 

  • Antonio, R., Tyandra, N., Nusantara, L. T., & Gunawan, A. A. S. (2022, September). Study literature review: Discovering the effect of Chatbot implementation in E-commerce customer service system towards customer satisfaction. In 2022 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 296–301). IEEE.

    Google Scholar 

  • Baier, L., Rackow, T., Donhauser, T., Pfeffer, D., Schuderer, P., & Franke, J. (2016, December). Logistical integration of smart homes for automated consumer goods supply based on smart refrigerators. In Advanced engineering forum (Vol. 19, pp. 107–115). Trans Tech Publications.

    Google Scholar 

  • Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in E-commerce: A bibliometric study and literature review. Electronic Markets, 32(1), 297–338.

    Article  Google Scholar 

  • Biswas, B., Sanyal, M. K., & Mukherjee, T. (2023). AI-based sales forecasting model for digital marketing. International Journal of E-Business Research (IJEBR), 19(1), 1–14.

    Google Scholar 

  • Bobadilla, J., Hernando, A., Ortega, F., & Bernal, J. (2011). A framework for collaborative filtering recommender systems. Expert Systems with Applications, 38(12), 14609–14623.

    Article  Google Scholar 

  • Brühl, V. (2023). Generative Artificial Intelligence (GAI)—Foundations, use cases and economic potential. Center for Financial Studies Working Paper (713).

    Google Scholar 

  • Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Modeling the global economic impact of AI. McKinsey Global Institute, 2.

    Google Scholar 

  • Cai, J., Shao, X., & Ma, W. (2009). Ontology driven semantic search over structure p2p network. In 2009 Ninth International Conference on Hybrid Intelligent Systems (Vol. 3, pp. 29–34). IEEE.

    Google Scholar 

  • Chodak, G., & Chawla, Y. (2022, February). Artificial intelligence in online stores’ processes. In Advanced Computing: 11th International Conference, IACC 2021, Msida, Malta, December 18–19, 2021, Revised Selected Papers (pp. 214–228). Springer International Publishing.

    Google Scholar 

  • Cortes, B., Teles, J., & Duarte, E. (2023, July). Exploring emotions in avatar design to increase adherence to Chatbot technology. In International Conference on Human-Computer Interaction (pp. 273–282). Springer Nature.

    Google Scholar 

  • Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), 43–53.

    Google Scholar 

  • De Campos, L. M., Fernández-Luna, J. M., Huete, J. F., & Rueda-Morales, M. A. (2010). Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, 51(7), 785–799.

    Article  Google Scholar 

  • Deldjoo, Y., Elahi, M., Cremonesi, P., Garzotto, F., Piazzolla, P., & Quadrana, M. (2016). Content-based video recommendation system based on stylistic visual features. Journal on Data Semantics, 5, 99–113.

    Article  Google Scholar 

  • Dhaliwal, A. (2020). The rise of automation and robotics in warehouse management. In Transforming Management Using Artificial Intelligence Techniques (pp. 63–72). CRC Press.

    Google Scholar 

  • Ford, J., Jain, V., Wadhwani, K., & Gupta, D. G. (2023). AI advertising: An overview and guidelines. Journal of Business Research, 166, 114124.

    Article  Google Scholar 

  • Gupta, M., Akiri, C., Aryal, K., Parker, E., & Praharaj, L. (2023). From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy. IEEE Access, 11, 80218–80245.

    Article  Google Scholar 

  • Haddara, M., Salazar, A., & Langseth, M. (2023). Exploring the impact of GDPR on big data analytics operations in the E-commerce industry. Procedia Computer Science, 219, 767–777.

    Article  Google Scholar 

  • Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.

    Article  Google Scholar 

  • Han, M. C. (2021). The impact of anthropomorphism on consumers’ purchase decision in Chatbot commerce. Journal of Internet Commerce, 20(1), 46–65.

    Article  Google Scholar 

  • Hartmann, J., & Netzer, O. (2023). Natural language processing in marketing. In Artificial Intelligence in Marketing (Vol. 20, pp. 191–215). Emerald Publishing.

    Google Scholar 

  • Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M. (2022). Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives. Computers & Security, 118, 102746.

    Article  Google Scholar 

  • Huang, C. C., Liang, W. Y., Lai, Y. H., & Lin, Y. C. (2010). The agent-based negotiation process for B2C e-commerce. Expert Systems with Applications, 37(1), 348–359.

    Article  Google Scholar 

  • Isaacson, W. (2014). The innovators: How a group of inventors, hackers, geniuses and geeks created the digital revolution. Simon and Schuster.

    Google Scholar 

  • Jajalow, D. (2022). ChatGPT passes the Turing test, https://mpost.io/pl/chatgpt-passes-the-turing-test/, published 08.12.2022, downloaded 03.07.2023.

  • Karn, A. L., Karna, R. K., Kondamudi, B. R., Bagale, G., Pustokhin, D. A., Pustokhina, I. V., & Sengan, S. (2023). Customer centric hybrid recommendation system for E-commerce applications by integrating hybrid sentiment analysis. Electronic Commerce Research, 23(1), 279–314.

    Article  Google Scholar 

  • Kashyap, D. A. K., Sahu, I., & Kumar, D. A. (2022). Artificial intelligence and its applications in E-commerce—A review analysis and research agenda. Journal of Theoretical and Applied Information Technology, 100(24).

    Google Scholar 

  • Khrais, L. T. (2020). Role of artificial intelligence in shaping consumer demand in E-commerce. Future Internet, 12(12), 226.

    Article  Google Scholar 

  • Kocoń, J., Baran, J., Gruza, M., Janz, A., Kajstura, M., Kazienko, P., Korczyński, W., Miłkowski, P., Piasecki, M., & Szołomicka, J. (2022, June). Neuro-symbolic models for sentiment analysis. In International Conference on Computational Science (pp. 667–681). Springer International Publishing.

    Google Scholar 

  • Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: Definition, trends, techniques, and cases. Artificial Intelligence, 1, 270–299.

    Google Scholar 

  • Kopel, M., Sobecki, J., & Wasilewski, A. (2013). Automatic web-based user interface delivery for soa-based systems. In Computational Collective Intelligence. Technologies and Applications: 5th International Conference, ICCCI 2013, Craiova, Romania, September 11–13, 2013, Proceedings 5 (pp. 110-119). Springer Berlin Heidelberg.

    Google Scholar 

  • Koto, F., Lau, J. H., & Baldwin, T. (2022, May). Can pretrained language models generate persuasive, faithful, and informative ad text for product descriptions? In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5) (pp. 234–243).

    Google Scholar 

  • Li, M., & Wang, R. (2023). Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand. Journal of Retailing and Consumer Services, 71, 103209.

    Article  Google Scholar 

  • Li, S., Liu, F., Zhang, Y., Zhu, B., Zhu, H., & Yu, Z. (2022). Text mining of user-generated content (UGC) for business applications in e-commerce: A systematic review. Mathematics, 10(19), 3554.

    Article  Google Scholar 

  • Liang, C., Chee, K. J., Zou, Y., Zhu, H., Causo, A., Vidas, S., & Cheah, C. C. (2015, October). Automated robot picking system for e-commerce fulfillment warehouse application. In The 14th IFToMM World Congress.

    Google Scholar 

  • Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, 41(4), 2065–2073.

    Article  Google Scholar 

  • Lin, X. (2020, April). Sentiment analysis of e-commerce customer reviews based on natural language processing. In Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence (pp. 32–36).

    Google Scholar 

  • Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook (pp. 73–105). Springer.

    Google Scholar 

  • Lu, X. Y., & Fang, J. S. (2019). “New Retail”: Innovating the development model of China’s fresh e-commerce. DEStech Transactions on Social Science, Education and Human Science, 28–32.

    Google Scholar 

  • Martinez, R. (2019). Artificial intelligence: Distinguishing between types & definitions. Nevada Law Journal, 19(3), 9.

    Google Scholar 

  • Meshram, S., Naik, N., Megha, V. R., More, T., & Kharche, S. (2021, June). Conversational AI: Chatbots. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1–6). IEEE.

    Google Scholar 

  • Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., & Wong, L. W. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 1–32.

    Google Scholar 

  • Palos-Sanchez, P., Saura, J. R., & Martin-Velicia, F. (2019). A study of the effects of programmatic advertising on users’ concerns about privacy overtime. Journal of Business Research, 96, 61–72.

    Article  Google Scholar 

  • Philp, M., Jacobson, J., & Pancer, E. (2022). Predicting social media engagement with computer vision: An examination of food marketing on Instagram. Journal of Business Research, 149, 736–747.

    Article  Google Scholar 

  • Pishva, D. (2017, February). Internet of Things: Security and privacy issues and possible solution. In 2017 19th international Conference on Advanced Communication Technology (ICACT) (pp. 797–808). IEEE.

    Google Scholar 

  • Pitman, A., & Zanker, M. (2010, December). Insights from applying sequential pattern mining to e-commerce click stream data. In 2010 IEEE International Conference on Data Mining Workshops (pp. 967–975). IEEE.

    Google Scholar 

  • Preethi, G., Krishna, P. V., Obaidat, M. S., Saritha, V., & Yenduri, S. (2017, July). Application of deep learning to sentiment analysis for recommender system on cloud. In 2017 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 93–97). IEEE.

    Google Scholar 

  • Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web: methods and strategies of web personalization (pp. 291–324). Springer Berlin Heidelberg.

    Google Scholar 

  • Singh, R. (2021). A study of artificial intelligence and e-commerce ecosystem—A customer’s perspective. International Journal of Research in Engineering, Science and Management, 4(2), 78–87.

    Google Scholar 

  • Song, S. W., & Shin, M. (2022). Uncanny valley effects on Chatbot trust, purchase intention, and adoption intention in the context of e-commerce: The moderating role of avatar familiarity. International Journal of Human–Computer Interaction, 1–16.

    Google Scholar 

  • Suchacka, G., & Stemplewski, S. (2017). Application of neural network to predict purchases in online store. In Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology—ISAT 2016—Part IV (pp. 221–231). Springer International Publishing.

    Google Scholar 

  • Truitt, E. R. (2021, November 24), Surveillance, companionship, and entertainment: The ancient history of intelligent machines. https://thereader.mitpress.mit.edu/the-ancient-history-of-intelligent-machines/ (accessed 08.09.2023).

  • Turing, A. M. (2012). Computing machinery and intelligence (1950). In The essential turing: The ideas that gave birth to the Computer Age (pp. 433–464).

    Google Scholar 

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

    Google Scholar 

  • Viana, P., & Soares, M. (2017). A hybrid approach for personalized news recommendation in a mobility scenario using long-short user interest. International Journal on Artificial Intelligence Tools, 26(02), 1760012.

    Article  Google Scholar 

  • Wang, P. (1995). Non-axiomatic reasoning system: Exploring the essence of intelligence (Ph.D. Dissertation). Indiana University.

    Google Scholar 

  • Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37.

    Article  Google Scholar 

  • Wasilewski, A., & Pawelek-Lubera, E. (2023). Architecture of the platform for self-adaptation of E-commerce interfaces (p. 179). Scientific Papers of Silesian University of Technology.

    Google Scholar 

  • Watts, J., & Adriano, A. (2021). Uncovering the sources of machine-learning mistakes in advertising: Contextual bias in the evaluation of semantic relatedness. Journal of Advertising, 50(1), 26–38.

    Article  Google Scholar 

  • Yalçın, N., & Köse, U. (2010). What is search engine optimization: SEO? Procedia-Social and Behavioral Sciences, 9, 487–493.

    Article  Google Scholar 

  • Yildirim, A., Reefke, H., & Aktas, E. (2023). Mobile robot systems and their evaluation. Mobile robot automation in warehouses: A framework for decision making and integration (pp. 17–47). Springer International Publishing.

    Chapter  Google Scholar 

  • Yin, W., Ren, J., Wu, Y., Song, R., Liu, L., Cheng, Z., & Wang, S. (2022, September). Knowledge enhanced pre-trained language model for product summarization. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 263–273). Springer Nature Switzerland.

    Google Scholar 

  • Zhang, X., Zou, Y., Zhang, H., Zhou, J., Diao, S., Chen, J., Ding, Z., He, Z., He, X., Xiao, Y., Long, B., Yu, H., & Wu, L. (2022). Automatic product copywriting for e-commerce. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12423–12431.

    Article  Google Scholar 

  • Zhongmin, W., & Yanan, Y. (2018, August). Design of an interactive smart refrigerator based on embedded system. In 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) (pp. 589–592). IEEE.

    Google Scholar 

  • Zumstein, D., & Kotowski, W. (2020, April). Success factors of e-commerce-drivers of the conversion rate and basket value. In 18th International Conference e-Society 2020 (pp. 43–50).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Wrocław University of Science and Technology, Wrocław, Poland

    Grzegorz Chodak

Authors
  1. Grzegorz Chodak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grzegorz Chodak .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chodak, G. (2024). Artificial Intelligence in E-Commerce. In: The Future of E-commerce. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-55225-0_7

Download citation

Publish with us

Policies and ethics

Search

Navigation