AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce
Source abstract
Abstract
Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such as blockchain, virtual reality, and augmented reality, to enhance the consumer experience throughout the e-commerce process.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Big Data and Business Intelligence · Blockchain Technology Applications and Security · Impact of AI and Big Data on Business and Society
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700505 Iasi, Romania
References (57)
Show source-provided references
1. Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods10.1016/j.elerap.2010.11.003 ↗
2. Christidis, K., and Mentzas, G. (2012, January 7–9). A Topic-Based Recommender System for Electronic Marketplace Platforms. Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, Athens, Greece.10.1109/ICTAI.2012.59 ↗
3. Economic corollaries of personalized recommendations10.1016/j.jretconser.2022.103003 ↗
4. Performance Evaluation of Recommender Systems
5. Lessons on Applying Automated Recommender Systems to Information-Seeking Tasks
6. Chen, J., and Raghunathan, S. (2016). Advertising Role of Recommender Systems in Electronic Marketplaces: A Boon or a Bane for Competing Sellers?. SSRN Electron. J.
7. Informative Role of Recommender Systems in Electronic Marketplaces: A Boon or a Bane for Competing Sellers10.25300/MISQ/2020/14614 ↗
8. Empirical Analysis of the Impact of Recommender Systems on Sales10.2753/MIS0742-1222270205 ↗
9. Recommender System Rethink: Implications for an Electronic Marketplace with Competing Manufacturers10.1287/isre.2017.0765 ↗
10. Blockchain-based recommender systems: Applications, challenges and future opportunities10.1016/j.cosrev.2021.100439 ↗
11. Graph Data Mining in Recommender Systems10.1007/978-3-030-91560-5_36 ↗
12. Sulikowski, P., Zdziebko, T., Hussain, O., and Wilbik, A. (2021, January 11–14). Fuzzy Approach to Purchase Intent Modeling Based on User Tracking For E-commerce Recommenders. Proceedings of the 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg.10.1109/FUZZ45933.2021.9494585 ↗
13. Artificial intelligence in recommender systems10.1007/s40747-020-00212-w ↗
14. Artificial intelligence in E-Commerce: A bibliometric study and literature review10.1007/s12525-022-00537-z ↗
15. Recommender Systems Leveraging Multimedia Content10.1145/3407190 ↗
16. Keele, S. (2023, March 30). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Software Engineering Group. Available online: https://www.researchgate.net/profile/Barbara-Kitchenham/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering/links/61712932766c4a211c03a6f7/Guidelines-for-performing-Systematic-Literature-Reviews-in-Software-Engineering.pdf.
17. A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations10.1016/j.jretconser.2021.102528 ↗
18. Mining customer product ratings for personalized marketing10.1016/S0167-9236(02)00108-2 ↗
19. Carbone, P., and Vlassov, V. (2015, January 21–25). Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions. Proceedings of the 2015 International Conference on Cloud and Autonomic Computing, Boston, MA, USA.10.1109/ICCAC.2015.9 ↗
20. Lofi, C., and Wille, P. (2015, January 7–9). Exploiting Social Judgements in Big Data Analytics. Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB, Trier, Germany.
21. Sangeetha, T., Balaganesh, N., and Muneeswaran, K. (2017, January 2–3). Aspects based opinion mining from online reviews for product recommendation. Proceedings of the 2017 International Conference on Computational Intelligence in Data Science(ICCIDS), Chennai, India.10.1109/ICCIDS.2017.8272657 ↗
22. Păvăloaia, V.-D., Teodor, E.-M., Fotache, D., and Danileţ, M. (2019). Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences. Sustainability, 11.10.3390/su11164459 ↗
23. Singal, S.M., and Juneja, B. (2016, January 6–7). AdaBoosting for case-based recommendation system. Proceedings of the 2016 International Conference on Information Technology (InCITe)—The Next Generation IT Summit on the Theme—Internet of Things: Connect Your Worlds, Noida, India.10.1109/INCITE.2016.7857591 ↗
24. Analysis of Online Consumer Behavior—Design of CRISP-DM Process Model10.7160/aol.2020.120302 ↗
25. Difference Factor’ KNN Collaborative Filtering Recommendation Algorithm10.1007/978-3-319-14717-8_14 ↗
26. Customer segmentation using various machine learning techniques
27. Qomariyah, N.N., and Kazakov, D.L. (2017, January 27–31). Learning Binary Preference Relations: Analysis of Logic-based versus Statistical Approaches. Proceedings of the ACM RecSys 2017 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, Como, Italy.
28. Ahuja, R., Solanki, A., and Nayyar, A. (2019, January 10–11). Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.10.1109/CONFLUENCE.2019.8776969 ↗
29. A multi-theoretical kernel-based approach to social network-based recommendation10.1016/j.dss.2014.05.006 ↗
30. Studying the Contribution of Machine Learning and Artificial Intelligence in the Interface Design of E-commerce Site10.1007/978-981-13-1927-3_20 ↗
31. Sewak, M., and Singh, S. (2016, January 22–24). IoT and distributed machine learning powered optimal state recommender solution. Proceedings of the 2016 International Conference on Internet of Things and Applications (IOTA), Pune, India.10.1109/IOTA.2016.7562703 ↗
32. Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis10.1016/j.jretconser.2020.102096 ↗
33. Digital humans in fashion: Will consumers interact?10.1016/j.jretconser.2020.102430 ↗
34. Panwar, M., Wadhwa, A., and Pippal, S. (2021, January 17–18). Recommendation System with Exploratory Data Analytics using Machine Learning. Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India.10.1109/ICAC3N53548.2021.9725582 ↗
35. A Personalized Recommender System Using Conceptual Dynamics
36. Chakraborty, R., M., A., Vats, K., Baradia, K., Khan, T., Sarkar, S., and Roychowdhury, S. (2019, January 3–5). Recommendence and Fashionsence: Online Fashion Advisor for Offline Experience. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, Kolkata India.
37. A Practical Deep Online Ranking System in E-commerce Recommendation10.1007/978-3-030-10997-4_12 ↗
38. Rawat, R.M., Tomar, V., and Kumar, V. (2020, January 10–12). An Embedding-based Deep Learning Approach for Movie Recommendation. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.10.1109/ICCES48766.2020.9137998 ↗
39. Yadhati, S.J., Vikyath, S., Maddela, V.P., Pathak, V., and Prathibhamol, C. (2021, January 6–8). Movie Recommender System with Visualized Embeddings. Proceedings of the 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India.10.1109/ICCCNT51525.2021.9579784 ↗
40. Grocery Product Classification and Recommendation System Based on Machine Learning and Customer Profile Identity
41. Guha, R. (2021, January 2–4). Improving the Performance of an Artificial Intelligence Recommendation Engine with Deep Learning Neural Nets. Proceedings of the 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India.10.1109/I2CT51068.2021.9417936 ↗
42. Sulikowski, P., and Zdziebko, T. (2020). Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing. Electronics, 9.10.3390/electronics9020266 ↗
43. Guillermo, M., Espanola, J., Billones, R.K., Vicerra, R.R., Bandala, A., Sybingco, E., Dadios, E.P., and Fillone, A. (2021, January 7–10). Content-based Fashion Recommender System Using Unsupervised Learning. Proceedings of the TENCON 2021–2021 IEEE Region 10 Conference (TENCON), Auckland, New Zealand.10.1109/TENCON54134.2021.9707459 ↗
44. Improving Online Customer Shopping Experience with Computer Vision and Machine Learning Methods
45. Ullah, F., Zhang, B., Khan, R.U., Ullah, I., Khan, A., and Qamar, A.M. (2020, January 24–26). Visual-based Items Recommendation Using Deep Neural Network. Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things, Sanya, China.10.1145/3398329.3398359 ↗
46. Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments10.1287/isre.2019.0907 ↗
47. Goldenberg, D., Kofman, K., Albert, J., Mizrachi, S., Horowitz, A., and Teinemaa, I. (2021, January 8–12). Personalization in Practice: Methods and Applications. Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, Israel.10.1145/3437963.3441657 ↗
48. Kartheek, M., and Sajeev, G.P. (2021, January 26–28). Building Semantic Based Recommender System Using Knowledge Graph Embedding. Proceedings of the 2021 Sixth International Conference on Image Information Processing (ICIIP), Shimla, India.10.1109/ICIIP53038.2021.9702632 ↗
49. A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship10.1016/j.dss.2013.02.009 ↗
50. Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects
51. Idemudia, E.C. (2020). Advances in Business Strategy and Competitive Advantage, IGI Global.
52. Jannach, D., Karakaya, Z., and Gedikli, F. (2012, January 4–8). Accuracy improvements for multi-criteria recommender systems. Proceedings of the 13th ACM Conference on Electronic Commerce, Valencia, Spain.10.1145/2229012.2229065 ↗
53. Mykhalchuk, T., Zatonatska, T., Dluhopolskyi, O., Zhukovska, A., Dluhopolska, T., and Liakhovych, L. (2021, January 22–25). Development of Recommendation System in e-Commerce using Emotional Analysis and Machine Learning Methods. Proceedings of the 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Cracow, Poland.10.1109/IDAACS53288.2021.9660854 ↗
54. A New Recommender System for 3D E-Commerce: An EEG Based Approach10.12720/joams.1.1.61-65 ↗
55. Paraschakis, D., Nilsson, B.J., and Hollander, J. (2015, January 9–11). Comparative Evaluation of Top-N Recommenders in e-Commerce: An Industrial Perspective. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.10.1109/ICMLA.2015.183 ↗
56. The UbiCARS Model-Driven Framework: Automating Development of Recommender Systems for Commerce10.1007/978-3-030-03062-9_3 ↗
57. Cha, N., Cho, H., Lee, S., and Hwang, J. (2019, January 17–20). Effect of AI Recommendation System on the Consumer Preference Structure in e-Commerce: Based on Two types of Preference. Proceedings of the 2019 21st International Conference on Advanced Communication Technology (ICACT), Pyeongchang, Republic of Korea.10.23919/ICACT.2019.8701967 ↗