nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Decoding the Recommender System: A Comprehensive Guide to Explainable AI in E-commerce

verfasst von : Garima Sahu, Loveleen Gaur

Erschienen in: Role of Explainable Artificial Intelligence in E-Commerce

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The rapid growth of e-commerce has resulted in an increasingly competitive landscape where businesses strive to provide personalized and engaging experiences to their customers. Recommender systems, powered by advanced algorithms and artificial intelligence, are central to this effort, curating tailored suggestions for products, services, and content. However, the complex and opaque decision-making processes of these systems often act as black boxes, limiting user understanding and trust. This chapter delves into the exclusive roles of explainable AI in the decision-making processes of recommender systems within the context of e-commerce, highlighting its importance in fostering trustworthiness, ensuring ethical and legal compliance, and facilitating debugging and model improvement. We explore various types of explanations, techniques for generating explanations, and real-world examples of explainable recommender systems. In conclusion, explainable AI is an indispensable component of recommender systems, playing a critical role in enhancing user trust and engagement, ultimately leading to improved customer satisfaction and increased revenues for e-commerce businesses. As AI systems continue to evolve and become more integrated into our lives, explainability will remain a crucial aspect of their design and implementation.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Explainable Artificial Intelligence (XAI) for Managing Customer Needs in E-Commerce: A Systematic Review

Nächstes Kapitel “The AI Revolution in E-Commerce: Personalization and Predictive Analytics”

Abdollahi, B., & Nasraoui, O. (2016). Explainable matrix factorization for collaborative filtering. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 5–6). https://doi.org/10.1145/2872518.2889402.

Abdollahi, B., & Nasraoui, O. (2018). Transparent recommendations: An approach to explainable recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems (pp. 364–365). https://doi.org/10.1145/3240323.3240375.

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052CrossRef

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Herrera, F., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012CrossRef

Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., & Eckersley, P., et al. (2020). Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 648–657). https://doi.org/10.1145/3351095.3372850.

Bilgic, M., & Mooney, R. J. (2005). Explaining recommendations: Satisfaction vs. promotion. In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces (pp. 13–18).

Biren, O., & Cotton, C. (2017). Explanation and justification in machine learning: A survey. In Proceedings of the IJCAI-17 Workshop on Explainable Artificial Intelligence (XAI) (pp. 1–9).

Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012CrossRef

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785.

10.

Gaur, L., & Sahoo, B. M. (2022). Introduction to explainable AI and intelligent transportation. Explainable artificial intelligence for intelligent transportation systems: Ethics and applications (pp. 1–25). Springer International Publishing.CrossRef

11.

Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining explanations: An approach to evaluating interpretability of machine learning. arXiv preprint arXiv:1806.00069.

12.

Gonzalez, M. F., Liu, W., Shirase, L., Tomczak, D. L., Lobbe, C. E., Justenhoven, R., & Martin, N. R. (2022). Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior, 130, 107179.CrossRef

13.

Gramegna, A., & Giudici, P. (2021). SHAP and LIME: An evaluation of discriminative power in credit risk. Frontiers in Artificial Intelligence, 4, 752558.CrossRef

14.

Guidotti, R., Monreale, A., Pedreschi, D., & Giannotti, F. (2021). Principles of explainable artificial intelligence. Explainable AI Within the Digital Transformation and Cyber Physical Systems: XAI Methods and Applications, 9–31.

15.

Gupta, V., & Sahu, G. (2021). Reviving the Indian hospitality industry after the Covid-19 pandemic: The role of innovation in training. Worldwide hospitality and tourism themes, 13(5), 599–609.CrossRef

16.

Gupta, V., Roy, H., & Sahu, G. (2022). HOW the tourism & hospitality lecturers coped with the transition to online teaching due to COVID-19: An assessment of stressors, negative sentiments and coping strategies. Journal of Hospitality, Leisure, Sport and Tourism Education, 30, 100341.CrossRef

17.

Haque, A. B., Islam, A. N., & Mikalef, P. (2023). Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research. Technological Forecasting and Social Change, 186, 122120.CrossRef

18.

Jannach, D., & Adomavicius, G. (2016). Recommendations with a purpose. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 7–10). https://doi.org/10.1145/2959100.2959176.

19.

Jiang, J., Kahai, S., & Yang, M. (2022). Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty. International Journal of Human-Computer Studies, 165, 102839.CrossRef

20.

Jiarpakdee, J., Tantithamthavorn, C. K., Dam, H. K., & Grundy, J. (2020). An empirical study of model-agnostic techniques for defect prediction models. IEEE Transactions on Software Engineering, 48(1), 166–185.CrossRef

21.

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263CrossRef

22.

Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021). What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research. Artificial Intelligence, 296, 103473.MathSciNetCrossRef

23.

Lee, S. (2022). AI as an explanation agent and user-centered explanation interfaces for trust in AI-based systems. In Human-Centered Artificial Intelligence (pp. 91–102). Academic Press.

24.

Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80. https://doi.org/10.1109/MIC.2003.1167344

25.

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 4768–4777).

26.

Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021). The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 103655.CrossRef

27.

Meske, C., Abedin, B., Klier, M., & Rabhi, F. (2022). Explainable and responsible artificial intelligence. Electronic Markets, 1–4.

28.

Miller, T., Howe, P., & Sonenberg, L. (2017). Explainable AI: Beware of inmates running the asylum or: How I learnt to stop worrying and love the social and behavioural sciences. arXiv preprint arXiv:1712.00547.

29.

Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1–15. https://doi.org/10.1016/j.dsp.2017.10.011MathSciNetCrossRef

30.

Nunes, I., & Jannach, D. (2017). A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction, 27(3–5), 393–444. https://doi.org/10.1007/s11257-017-9195-0CrossRef

31.

Paz-Ruza, J., Eiras-Franco, C., Guijarro-Berdiñas, B., & Alonso-Betanzos, A. (2022). Sustainable personalisation and explainability in dyadic data systems. Procedia Computer Science, 207, 1017–1026.CrossRef

32.

Quadrianto, N., Schuller, B. W., & Lattimore, F. R. (2021). Ethical machine learning and artificial intelligence. Frontiers in Big Data, 4, 742589.CrossRef

33.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). https://doi.org/10.1145/2939672.2939778

34.

Saeed, W., & Omlin, C. (2023). Explainable ai (xai): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 110273.

35.

Sahu, G., Gaur, L., & Singh, G. (2021). Applying niche and gratification theory approach to examine the users’ indulgence towards over-the-top platforms and conventional TV. Telematics and Informatics, 65, 101713.CrossRef

36.

Sahu, G., Gaur, L., & Singh, G. (2022, November). Analyzing the Users’ De-familiarity with Thumbnails on OTT Platforms to Influence Content Streaming. In 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 551–556). IEEE.

37.

Vale, D., El-Sharif, A., & Ali, M. (2022). Explainable artificial intelligence (XAI) post-hoc explainability methods: Risks and limitations in non-discrimination law. AI and Ethics, 1–12.

38.

Weber, P., Carl, K. V., & Hinz, O. (2023). Applications of explainable artificial intelligence in finance—A systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 1–41.

39.

Yalcin, O. G. (2021). GDPR compliant data processing and privacy preserving technologies: A literature review on notable Horizon 2020 projects. New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence: The DITTET Collection, 166–177.

40.

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning-based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1–38. https://doi.org/10.1145/3285029CrossRef

41.

Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1–101. https://doi.org/10.1561/1500000066

42.

Zimmermann, R., Mora, D., Cirqueira, D., Helfert, M., Bezbradica, M., Werth, D., Weitzl, W. J., Riedl, R., & Auinger, A. (2023). Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence. Journal of Research in Interactive Marketing, 17(2), 273–298. https://doi.org/10.1108/JRIM-09-2021-0237CrossRef

43.

Gaur, L., Sahoo, B. M. (2022). Intelligent transportation technology enablers. In: Explainable Artificial Intelligence for Intelligent Transportation Systems. Springer. https://doi.org/10.1007/978-3-031-09644-0_2.

44.

Gaur, L., Ratta, M., & Gaur, A. (2022). Future of DeepFakes and Ectypes In: Deepfakes. CRC Press, 9781003231493.

45.

Gaur, L., & Sahoo, B. M. (2022). Explainable AI in ITS: Ethical concerns. In: Explainable Artificial Intelligence for Intelligent Transportation Systems. Springer. https://doi.org/10.1007/978-3-031-09644-0_5.

46.

Gaur, L., Sahoo, B.M. (2022). Intelligent transportation system: Modern business models. In: Explainable Artificial Intelligence for Intelligent Transportation Systems. Springer. https://doi.org/10.1007/978-3-031-09644-0_4.

47.

Gaur, L., Jhanjhi, N. Z., Bakshi, S., & Gupta, P. (2022). Analyzing consequences of artificial intelligence on jobs using topic modeling and keyword extraction. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 435–440). https://doi.org/10.1109/ICIPTM54933.2022.9754064.

48.

Gaur, L., Bhandari, M., Razdan, T., Mallik, S., & Zhao, Z. (2022). Explanation-driven deep learning model for prediction of brain Tumour status using MRI image data. Frontier in Genetics, 13, 822666.CrossRef

49.

Anshu, K., Gaur, L., & Singh, G. (2021). Co-creation: Interface for online affective experience and repurchase intention. International Journal of Business and Economics, 20(2), 161–185. ISSN 1607–0704.

50.

Sharma, S., Singh, G., Gaur, L., & Afaq, A. (2022). Exploring customer adoption of autonomous shopping systems. Telematics and Informatics, 73, 101861, ISSN 0736–5853. https://doi.org/10.1016/j.tele.2022.101861.

51.

Sahu, G., Singh, G., Singh, G. & Gaur, L. (2024). Exploring new dimensions in OTT consumption: an empirical study on perceived risks, descriptive norms and goal-directed behaviour. Asia Pacific Journal of Marketing and Logistics. https://doi.org/10.1108/APJML-07-2023-0690.

Titel: Decoding the Recommender System: A Comprehensive Guide to Explainable AI in E-commerce
verfasst von: Garima Sahu
Loveleen Gaur
Verlag: Springer Nature Switzerland
Buch: Role of Explainable Artificial Intelligence in E-Commerce
Print ISBN: 978-3-031-55614-2

Electronic ISBN: 978-3-031-55615-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-55615-9_3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner