Reference: | Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI magazine, 35(4), 105–120. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7), e0130140. Bertrand, A., Belloum, R., Eagan, J. R., & Maxwell, W. (2022). How cognitive biases affect xai-assisted decision-making: A systematic review. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 78–91. Chen, J. Y., Procci, K., Boyce, M., Wright, J., Garcia, A., & Barnes, M. (2014). Situation awareness-based agent transparency. US Army Research Laboratory, 1–29. Chien, S.-Y., Yang, C.-J., & Yu, F. (2022). Xflag: Explainable fake news detection model on social media. International Journal of Human–Computer Interaction, 38(18-20), 1808–1827. Cramer, H., Evers, V., Ramlal, S., Van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008). The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-adapted interaction, 18, 455–496. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human factors, 37(1), 32–64. Flemisch, F., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., & Beller, J. (2012). Towards a dynamic balance between humans and automation: Authority, ability, responsibility and control in shared and cooperative control situations. Cognition, Technology & Work, 14, 3–18. Gedikli, F., Jannach, D., & Ge, M. (2014). How should i explain? a comparison of different explanation types for recommender systems. International Journal of HumanComputer Studies, 72(4), 367–382. 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. Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262. Gunning, D. (2017). Explainable artificial intelligence (xai). Defense advanced research projects agency (DARPA), nd Web, 2(2), 1. Hancock, B., Bordes, A., Mazare, P.-E., & Weston, J. (2019). Learning from dialogue after deployment: Feed yourself, chatbot! arXiv preprint arXiv:1901.05415. Harper, F. M., & Konstan, J. A. (2015). The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4), 1–19. Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. Proceedings of the 2000 ACM conference on Computer supported cooperative work, 241–250. Ho, S. Y., & Bodoff, D. (2014). The effects of web personalization on user attitude and behavior. MIS quarterly, 38(2), 497–A10. Kang, W.-C., & McAuley, J. (2018). Self-attentive sequential recommendation. 2018 IEEE international conference on data mining (ICDM), 197–206. Kizilcec, R. F. (2016). How much information? effects of transparency on trust in an algorithmic interface. Proceedings of the 2016 CHI conference on human factors in computing systems, 2390–2395. Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User modeling and useradapted interaction, 22, 441–504. Kovashka, A., Parikh, D., & Grauman, K. (2015). Whittlesearch: Interactive image search with relative attribute feedback. International Journal of Computer Vision, 115, 185–210. Kulesza, T., Stumpf, S., Burnett, M., & Kwan, I. (2012). Tell me more? the effects of mental model soundness on personalizing an intelligent agent. Proceedings of the sigchi conference on human factors in computing systems, 1–10. Lee, J. D. (2012). Trust, trustworthiness, and trustability. Presentation at the Workshop on Human Machine Trust for Robust Autonomous Systems, 31. Li, J., Tu, Z., Yang, B., Lyu, M. R., & Zhang, T. (2018). Multi-head attention with disagreement regularization. arXiv preprint arXiv:1810.10183. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., & Ma, J. (2017). Neural attentive session-based recommendation. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 1419–1428. Li, J., Miller, A. H., Chopra, S., Ranzato, M., & Weston, J. (2016). Dialogue learning with human-in-the-loop. arXiv preprint arXiv:1611.09823. Liang, Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018). Variational autoencoders for collaborative filtering. Proceedings of the 2018 world wide web conference, 689–698. Liang, Lai, & Ku. (2006). Personalized content recommendation and user satisfaction: heoretical synthesis and empirical findings. Journal of Management Information Systems, 23(3), 45–70. Liu, Z., Guo, Y., & Mahmud, J. (2021). When and why does a model fail? a human-in-theloop error detection framework for sentiment analysis. arXiv preprint arXiv:2106.00954. Liu, Z., Wang, J., Gong, S., Lu, H., & Tao, D. (2019). Deep reinforcement active learning for human-in-the-loop person re-identification. Proceedings of the IEEE/CVF international conference on computer vision, 6122–6131. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 4765–4774. https://arxiv.org/abs/1705.07874 Monarch, R. M. (2021). Human-in-the-loop machine learning: Active learning and annotation for human-centered ai. Simon; Schuster. Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & FernándezLeal, Á. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005–3054. Naiseh, M., Cemiloglu, D., Al Thani, D., Jiang, N., & Ali, R. (2021). Explainable recommendations and calibrated trust: Two systematic user errors. Computer, 54(10), 28–37. Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70– 84. Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. Proceedings of the fifth ACM conference on Recommender systems, 157– 164. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al. (2018). Improving language understanding by generative pre-training. Rao, A. S., & Georgeff, M. P. (1995). Bdi agents: From theory to practice. Proceedings of the 1st International Conference on Multi-Agent Systems (ICMAS), 95, 312–319. Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. Proceedings of the 19th international conference on World wide web, 811–820. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). ” why should i trust you?” explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135–1144. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable ai.International Journal of Human-Computer Studies, 146, 102551. Simonson, I., & Tversky, A. (1992). Choice in context: Tradeoff contrast and extremeness aversion. Journal of marketing research, 29(3), 281–295. Springer, A., & Whittaker, S. (2019). Progressive disclosure: Empirically motivated approaches to designing effective ransparency. Proceedings of the 24th international conference on intelligent user interfaces, 107–120. Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009. Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. Proceedings of the 28th ACM international conference on information and knowledge management, 1441–1450. Swartout, W., Paris, C., & Moore, J. (1991). Explanations in knowledge systems: Design for explainable expert systems. IEEE Expert, 6(3), 58–64. Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS quarterly, 865–890. Tang, J., & Wang, K. (2018). Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the eleventh ACM international conference on web search and data mining, 565–573. Tintarev, N., & Masthoff, J. (2012). Evaluating the effectiveness of explanations for recommender systems: Methodological issues and empirical studies on the impact of personalization. User Modeling and User-Adapted Interaction, 22, 399–439. 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. Vig, J., Sen, S., & Riedl, J. (2009). Tagsplanations: Explaining recommendations using tags. Proceedings of the 14th international conference on Intelligent user interfaces, 47–56. Wright, J. L., Chen, J. Y., Barnes, M. J., & Boyce, M. W. (2015). The effects of information level on human-agent interaction for route planning. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 811–815. Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022). A survey of human-inthe-loop for machine learning. Future Generation Computer Systems, 135, 364–381. Zhang, Y., & Chen, X. (2018). Explainable recommendation: A survey and new perspectives. corr abs/1804.11192 (2018). arXiv preprint arXiv:1804.11192. |