{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T07:40:09Z","timestamp":1748850009783,"version":"3.41.0"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031925771","type":"print"},{"value":"9783031925788","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-92578-8_8","type":"book-chapter","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T06:59:10Z","timestamp":1748847550000},"page":"105-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["User-Centered Trustworthy AI Metrics in\u00a0the\u00a0Metaverse"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7909-4059","authenticated-orcid":false,"given":"Peng Yuan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0217-9767","authenticated-orcid":false,"given":"Benjamin","family":"Finley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1361-1612","authenticated-orcid":false,"given":"Lik-Hang","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4690-2769","authenticated-orcid":false,"given":"Reza","family":"Hadi Mogavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6026-1083","authenticated-orcid":false,"given":"Pan","family":"Hui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., et\u00a0al.: Recommender Systems, vol.\u00a01. Springer, Heidelberg (2016)","DOI":"10.1007\/978-3-319-29659-3_1"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 247\u2013255 (2001)","DOI":"10.1145\/375551.375602"},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"108088","DOI":"10.1109\/ACCESS.2020.3000893","volume":"8","author":"F Al-Tam","year":"2020","unstructured":"Al-Tam, F., Correia, N., Rodriguez, J.: Learn to schedule (leasch): a deep reinforcement learning approach for radio resource scheduling in the 5g mac layer. IEEE Access 8, 108088\u2013108101 (2020)","journal-title":"IEEE Access"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Bailey, P., Moffat, A., Scholer, F., Thomas, P.: Retrieval consistency in the presence of query variations. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 395\u2013404 (2017)","DOI":"10.1145\/3077136.3080839"},{"key":"8_CR6","doi-asserted-by":"publisher","DOI":"10.1515\/9781400831050","volume-title":"Robust Optimization","author":"A Ben-Tal","year":"2009","unstructured":"Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press, Princeton (2009)"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM Sigir Conference on Research & Development in Information Retrieval, pp. 405\u2013414 (2018)","DOI":"10.1145\/3209978.3210063"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Castillo, C.: Fairness and transparency in ranking. In: ACM SIGIR Forum, vol.\u00a052, pp. 64\u201371. ACM (2019)","DOI":"10.1145\/3308774.3308783"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., et\u00a0al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Hurley, N.: Robust collaborative recommendation by least trimmed squares matrix factorization. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, vol.\u00a02, pp. 105\u2013112. IEEE (2010)","DOI":"10.1109\/ICTAI.2010.90"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TNSM.2019.2960849","volume":"17","author":"IS Com\u015fa","year":"2019","unstructured":"Com\u015fa, I.S., Trestian, R., Muntean, G.M., Ghinea, G.: 5mart: a 5g smart scheduling framework for optimizing qos through reinforcement learning. IEEE Trans. Netw. Serv. Manag. 17(2), 1110\u20131124 (2019)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"4","key":"8_CR12","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1287\/mnsc.1060.0514","volume":"52","author":"G Cui","year":"2006","unstructured":"Cui, G., Wong, M.L., Lui, H.K.: Machine learning for direct marketing response models: bayesian networks with evolutionary programming. Manag. Sci. 52(4), 597\u2013612 (2006)","journal-title":"Manag. Sci."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Du, Z., Zheng, J., Yu, H., Kong, L., Chen, G.: A unified congestion control framework for diverse application preferences and network conditions. In: Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, pp. 282\u2013296 (2021)","DOI":"10.1145\/3485983.3494840"},{"key":"8_CR14","unstructured":"European Union: Eu charter of fundamental rights (2012). https:\/\/ec.europa.eu\/info\/aid-development-cooperation-fundamental-rights\/your-rights-eu\/eu-charter-fundamental-rights_en"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"125345","DOI":"10.1109\/ACCESS.2020.3007577","volume":"8","author":"S Fathi-Kazerooni","year":"2020","unstructured":"Fathi-Kazerooni, S., Rojas-Cessa, R.: Gan tunnel: network traffic steganography by using gans to counter internet traffic classifiers. IEEE Access 8, 125345\u2013125359 (2020)","journal-title":"IEEE Access"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Geyik, S.C., Ambler, S., Kenthapadi, K.: Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2221\u20132231 (2019)","DOI":"10.1145\/3292500.3330691"},{"issue":"4","key":"8_CR17","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s10462-012-9364-9","volume":"42","author":"I Gunes","year":"2014","unstructured":"Gunes, I., Kaleli, C., Bilge, A., Polat, H.: Shilling attacks against recommender systems: a comprehensive survey. Artif. Intell. Rev. 42(4), 767\u2013799 (2014)","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"8_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.chbah.2024.100095","volume":"2","author":"H Hadan","year":"2024","unstructured":"Hadan, H., Wang, D.M., Mogavi, R.H., Tu, J., Zhang-Kennedy, L., Nacke, L.E.: The great AI witch hunt: reviewers\u2019 perception and (mis)conception of generative AI in research writing. Comput. Hum. Behav. Artif. Hum. 2(2), 100095 (2024). https:\/\/doi.org\/10.1016\/j.chbah.2024.100095","journal-title":"Comput. Hum. Behav. Artif. Hum."},{"issue":"1","key":"8_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.chbah.2023.100027","volume":"2","author":"R Hadi Mogavi","year":"2024","unstructured":"Hadi Mogavi, R., et al.: Chatgpt in education: a blessing or a curse? A qualitative study exploring early adopters\u2019 utilization and perceptions. Comput. Hum. Behav. Artif. Hum. 2(1), 100027 (2024). https:\/\/doi.org\/10.1016\/j.chbah.2023.100027","journal-title":"Comput. Hum. Behav. Artif. Hum."},{"issue":"4","key":"8_CR20","doi-asserted-by":"publisher","first-page":"4770","DOI":"10.1109\/TNSM.2021.3093302","volume":"18","author":"B He","year":"2021","unstructured":"He, B., et al.: Deepcc: multi-agent deep reinforcement learning congestion control for multi-path tcp based on self-attention. IEEE Trans. Netw. Serv. Manag. 18(4), 4770\u20134788 (2021)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"8_CR21","unstructured":"Heaven, W.D.: The department of defense is issuing ai ethics guidelines for tech contractors (2021). https:\/\/www.technologyreview.com\/2021\/11\/16\/1040190\/department-of-defense-government-ai-ethics-military-project-maven\/"},{"key":"8_CR22","unstructured":"HLEGAI: Ethics guidelines for trustworthy ai. B-1049 Brussels (2019)"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Hou, C., Gou, G., Shi, J., Fu, P., Xiong, G.: Wf-gan: fighting back against website fingerprinting attack using adversarial learning. In: 2020 IEEE Symposium on Computers and Communications (ISCC), pp.\u00a01\u20137. IEEE (2020)","DOI":"10.1109\/ISCC50000.2020.9219593"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Jeckmans, A.J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L., Tang, Q.: Privacy in recommender systems. In: Social Media Retrieval, pp. 263\u2013281. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-1-4471-4555-4_12","DOI":"10.1007\/978-1-4471-4555-4_12"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: ACM SIGIR Forum, vol.\u00a051, pp. 4\u201311. ACM (2017)","DOI":"10.1145\/3130332.3130334"},{"issue":"6245","key":"8_CR26","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255\u2013260 (2015)","journal-title":"Science"},{"key":"8_CR27","unstructured":"Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Enhancement of the neutrality in recommendation. In: Decisions@ RecSys, pp. 8\u201314. Citeseer (2012)"},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Lam, K.Y., Lee, L.H., Hui, P.: A2W: context-aware recommendation system for mobile augmented reality web browser, pp. 2447\u20132455. ACM (2021). https:\/\/doi.org\/10.1145\/3474085.3475413","DOI":"10.1145\/3474085.3475413"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th International Conference on World Wide Web, pp. 393\u2013402 (2004)","DOI":"10.1145\/988672.988726"},{"key":"8_CR30","unstructured":"Laufer, R.S.: Some analytic dimensions of privacy. In: Architectural psychology, Proceedings of the Lund Conference, Lund, Student literature (1973)"},{"key":"8_CR31","first-page":"1","volume":"54","author":"LH Lee","year":"2022","unstructured":"Lee, L.H., Braud, T., Hosio, S.J., Hui, P.: Towards augmented reality driven human-city interaction: current research on mobile headsets and future challenges. ACM Comput. Surv. (CSUR) 54, 1\u201338 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"8_CR32","unstructured":"Lee, L.H., et al.: All one needs to know about metaverse: a complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv preprint arXiv:2110.05352 (2021)"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Leonhardt, J., Anand, A., Khosla, M.: User fairness in recommender systems. In: Companion Proceedings of the Web Conference 2018, pp. 101\u2013102 (2018)","DOI":"10.1145\/3184558.3186949"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Li, J., Zhou, L., Li, H., Yan, L., Zhu, H.: Dynamic traffic feature camouflaging via generative adversarial networks. In: 2019 IEEE Conference on Communications and Network Security (CNS), pp. 268\u2013276. IEEE (2019)","DOI":"10.1109\/CNS.2019.8802772"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Li, X., Li, F., Ji, S., Zheng, Z., Chang, Y., Dong, A.: Incorporating robustness into web ranking evaluation. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 2007\u20132010 (2009)","DOI":"10.1145\/1645953.1646288"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Lin, W.Y., Hu, Y.H., Tsai, C.F.: Machine learning in financial crisis prediction: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(4), 421\u2013436 (2011)","DOI":"10.1109\/TSMCC.2011.2170420"},{"key":"8_CR37","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.envsoft.2016.03.014","volume":"81","author":"HR Maier","year":"2016","unstructured":"Maier, H.R., Guillaume, J.H., van Delden, H., Riddell, G.A., Haasnoot, M., Kwakkel, J.H.: An uncertain future, deep uncertainty, scenarios, robustness and adaptation: how do they fit together? Environ. Model. Softw. 81, 154\u2013164 (2016)","journal-title":"Environ. Model. Softw."},{"issue":"4","key":"8_CR38","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1016\/j.clsr.2018.05.017","volume":"34","author":"A Mantelero","year":"2018","unstructured":"Mantelero, A.: AI and big data: a blueprint for a human rights, social and ethical impact assessment. Comput. Law Secur. Rev. 34(4), 754\u2013772 (2018)","journal-title":"Comput. Law Secur. Rev."},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 627\u2013636 (2009)","DOI":"10.1145\/1557019.1557090"},{"key":"8_CR40","doi-asserted-by":"crossref","unstructured":"Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. (TOIT) 7(4), 23\u2013es (2007)","DOI":"10.1145\/1278366.1278372"},{"key":"8_CR41","unstructured":"Mobasher, B., Burke, R., Sandvig, J.J.: Model-based collaborative filtering as a defense against profile injection attacks. In: AAAI, vol.\u00a06, p.\u00a01388 (2006)"},{"key":"8_CR42","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"8_CR43","first-page":"119","volume":"79","author":"H Nissenbaum","year":"2004","unstructured":"Nissenbaum, H.: Privacy as contextual integrity. Wash. L. Rev. 79, 119 (2004)","journal-title":"Wash. L. Rev."},{"issue":"1","key":"8_CR44","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41591-018-0320-3","volume":"25","author":"B Norgeot","year":"2019","unstructured":"Norgeot, B., Glicksberg, B.S., Butte, A.J.: A call for deep-learning healthcare. Nat. Med. 25(1), 14\u201315 (2019)","journal-title":"Nat. Med."},{"issue":"4","key":"8_CR45","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1145\/1031114.1031116","volume":"4","author":"M O\u2019Mahony","year":"2004","unstructured":"O\u2019Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: a robustness analysis. ACM Trans. Internet Technol. (TOIT) 4(4), 344\u2013377 (2004)","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"issue":"2","key":"8_CR46","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1017\/bpp.2018.25","volume":"3","author":"EL Paluck","year":"2019","unstructured":"Paluck, E.L., Green, S.A., Green, D.P.: The contact hypothesis re-evaluated. Behav. Public Policy 3(2), 129\u2013158 (2019)","journal-title":"Behav. Public Policy"},{"issue":"5","key":"8_CR47","doi-asserted-by":"publisher","first-page":"3930","DOI":"10.1109\/JIOT.2020.3025988","volume":"8","author":"AJ Pinheiro","year":"2020","unstructured":"Pinheiro, A.J., de Araujo-Filho, P.F., Bezerra, J., Campelo, D.R.: Adaptive packet padding approach for smart home networks: a tradeoff between privacy and performance. IEEE Internet Things J. 8(5), 3930\u20133938 (2020)","journal-title":"IEEE Internet Things J."},{"key":"8_CR48","doi-asserted-by":"crossref","unstructured":"Rastegarpanah, B., Gummadi, K.P., Crovella, M.: Fighting fire with fire: using antidote data to improve polarization and fairness of recommender systems. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 231\u2013239 (2019)","DOI":"10.1145\/3289600.3291002"},{"key":"8_CR49","doi-asserted-by":"crossref","unstructured":"Shan, S., Bhagoji, A.N., Zheng, H., Zhao, B.Y.: Patch-based defenses against web fingerprinting attacks. In: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, pp. 97\u2013109 (2021)","DOI":"10.1145\/3474369.3486875"},{"key":"8_CR50","doi-asserted-by":"publisher","unstructured":"Shatilov, K.A., Chatzopoulos, D., Lee, L.H., Hui, P.: Emerging exg-based nui inputs in extended realities: a bottom-up survey. ACM Trans. Interact. Intell. Syst. 11(2) (2021). https:\/\/doi.org\/10.1145\/3457950","DOI":"10.1145\/3457950"},{"key":"8_CR51","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.cie.2018.03.039","volume":"125","author":"YR Shiue","year":"2018","unstructured":"Shiue, Y.R., Lee, K.C., Su, C.T.: Real-time scheduling for a smart factory using a reinforcement learning approach. Comput. Ind. Eng. 125, 604\u2013614 (2018)","journal-title":"Comput. Ind. Eng."},{"key":"8_CR52","doi-asserted-by":"crossref","unstructured":"Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2219\u20132228 (2018)","DOI":"10.1145\/3219819.3220088"},{"key":"8_CR53","doi-asserted-by":"crossref","unstructured":"Smuha, N.A.: The EU approach to ethics guidelines for trustworthy artificial intelligence. CRi-Comput. Law Rev. Int. (2019)","DOI":"10.9785\/cri-2019-200402"},{"issue":"5","key":"8_CR54","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1007\/s11948-017-9979-y","volume":"24","author":"JS Spiegel","year":"2018","unstructured":"Spiegel, J.S.: The ethics of virtual reality technology: social hazards and public policy recommendations. Sci. Eng. Ethics 24(5), 1537\u20131550 (2018)","journal-title":"Sci. Eng. Ethics"},{"issue":"5","key":"8_CR55","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TKDE.2019.2893638","volume":"32","author":"J Tang","year":"2019","unstructured":"Tang, J., Du, X., He, X., Yuan, F., Tian, Q., Chua, T.S.: Adversarial training towards robust multimedia recommender system. IEEE Trans. Knowl. Data Eng. 32(5), 855\u2013867 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"8_CR56","unstructured":"Tavani, H.: Search engines and ethics (2012)"},{"key":"8_CR57","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3389\/frobt.2017.00075","volume":"4","author":"J Torresen","year":"2018","unstructured":"Torresen, J.: A review of future and ethical perspectives of robotics and AI. Front. Rob. AI 4, 75 (2018)","journal-title":"Front. Rob. AI"},{"key":"8_CR58","unstructured":"Tsintzou, V., Pitoura, E., Tsaparas, P.: Bias disparity in recommendation systems. arXiv preprint arXiv:1811.01461 (2018)"},{"key":"8_CR59","doi-asserted-by":"publisher","unstructured":"Wang, J., Moulden, A.: AI trust score: a user-centered approach to building, designing, and measuring the success of intelligent workplace features. ACM (2021). https:\/\/doi.org\/10.1145\/3411763.3443452","DOI":"10.1145\/3411763.3443452"},{"key":"8_CR60","unstructured":"Wang, Y., Wang, L., Li, Y., He, D., Chen, W., Liu, T.Y.: A theoretical analysis of NDCG ranking measures. In: Proceedings of the 26th Annual Conference on Learning Theory (COLT 2013), vol.\u00a08, p.\u00a06. Citeseer (2013)"},{"issue":"3","key":"8_CR61","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11761-007-0013-0","volume":"1","author":"CA Williams","year":"2007","unstructured":"Williams, C.A., Mobasher, B., Burke, R.: Defending recommender systems: detection of profile injection attacks. SOCA 1(3), 157\u2013170 (2007)","journal-title":"SOCA"},{"key":"8_CR62","doi-asserted-by":"publisher","first-page":"11892","DOI":"10.1109\/ACCESS.2019.2892046","volume":"7","author":"K Xiao","year":"2019","unstructured":"Xiao, K., Mao, S., Tugnait, J.K.: Tcp-drinc: smart congestion control based on deep reinforcement learning. IEEE Access 7, 11892\u201311904 (2019)","journal-title":"IEEE Access"},{"key":"8_CR63","unstructured":"Xin, Y., Jaakkola, T.: Controlling privacy in recommender systems. In: Neural Information Processing Systems (2014)"},{"key":"8_CR64","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, K., Zhang, B., Chen, Z.: Privacy-enhancing personalized web search. In: Proceedings of the 16th International Conference on World Wide Web, pp. 591\u2013600 (2007)","DOI":"10.1145\/1242572.1242652"},{"key":"8_CR65","doi-asserted-by":"crossref","unstructured":"Zehlike, M., Castillo, C.: Reducing disparate exposure in ranking: a learning to rank approach. In: Proceedings of the Web Conference 2020, pp. 2849\u20132855 (2020)","DOI":"10.1145\/3366424.3380048"}],"container-title":["Lecture Notes in Computer Science","HCI in Games"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-92578-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T06:59:24Z","timestamp":1748847564000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-92578-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031925771","9783031925788"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-92578-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gothenburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2025.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}