{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:11:39Z","timestamp":1784646699019,"version":"3.55.0"},"reference-count":82,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62072429"],"award-info":[{"award-number":["62072429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Chinese Academy of Sciences \u201cLight of West China\u201d Program, and in part by the Key Cooperation Project of Chongqing Municipal Education Commission","award":["HZ2021008, HZ2021017"],"award-info":[{"award-number":["HZ2021008, HZ2021017"]}]},{"name":"Education and Teaching Reform Research Program of Chongqing Municipal Education Commission","award":["YJG232046"],"award-info":[{"award-number":["YJG232046"]}]},{"name":"\u201cFertilizer Robot\u201d project of Chongqing Committee on Agriculture and Rural Affairs"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>While<jats:italic>personalization<\/jats:italic>increases the utility of item recommendation, it also suffers from the issue of popularity bias. However, previous methods emphasize adopting supervised learning models to relieve popularity bias in the static recommendation, ignoring the dynamic transfer of user preference and amplification effects of the feedback loop in the recommender system (RS). In this paper, we focus on studying this issue in the interactive recommendation. We argue that diversification and novelty are both equally crucial for improving user satisfaction of IRS in the aforementioned setting. To achieve this goal, we propose a<jats:underline>D<\/jats:underline>iversity-<jats:underline>N<\/jats:underline>ovelty-<jats:underline>a<\/jats:underline>ware<jats:underline>I<\/jats:underline>nteractive<jats:underline>R<\/jats:underline>ecommendation framework (DNaIR) that augments offline reinforcement learning (RL) to increase the exposure rate of long-tail items with high quality. Its main idea is first to aggregate the item similarity, popularity, and quality into the reward model to help the planning of RL policy. It then designs a diversity-aware stochastic action generator to achieve an efficient and lightweight DNaIR algorithm. Extensive experiments are conducted on the three real-world datasets and an authentic RL environment (Virtual-Taobao). The experiments show that our model can better and full use of the long-tail items to improve recommendation satisfaction, especially those low popularity items with high-quality ones, thus achieving state-of-the-art performance.<\/jats:p>","DOI":"10.1145\/3618107","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T12:25:41Z","timestamp":1693571141000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4267-7795","authenticated-orcid":false,"given":"Xiaoyu","family":"Shi","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2989-6408","authenticated-orcid":false,"given":"Quanliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7210","authenticated-orcid":false,"given":"Hong","family":"Xie","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7788-9202","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"Southwest University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1605-0032","authenticated-orcid":false,"given":"Bo","family":"Peng","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7024-2270","authenticated-orcid":false,"given":"MingSheng","family":"Shang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3507-9607","authenticated-orcid":false,"given":"Defu","family":"Lian","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TKDE.2013.109","article-title":"Data mining with big data","volume":"26","author":"Wu Xindong","year":"2013","unstructured":"Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. 2013. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering 26, 1 (2013), 97\u2013107.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2792838.2792840"},{"issue":"2","key":"e_1_3_3_4_2","first-page":"420","article-title":"Large-scale and scalable latent factor analysis via distributed alternative stochastic gradient descent for recommender systems","volume":"8","author":"Shi Xiaoyu","year":"2020","unstructured":"Xiaoyu Shi, Qiang He, Xin Luo, Yanan Bai, and Mingsheng Shang. 2020. Large-scale and scalable latent factor analysis via distributed alternative stochastic gradient descent for recommender systems. IEEE Transactions on Big Data 8, 2 (2020), 420\u2013431.","journal-title":"IEEE Transactions on Big Data"},{"key":"e_1_3_3_5_2","article-title":"A survey on the fairness of recommender systems","author":"Wang Yifan","year":"2022","unstructured":"Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2022. A survey on the fairness of recommender systems. ACM Transactions on Information Systems (TOIS) (2022).","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_3_6_2","article-title":"Bias and debias in recommender system: A survey and future directions","author":"Chen Jiawei","year":"2022","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2022. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems (TOIS) (2022).","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109912"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441820"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269264"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2896766"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467289"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503181.3503203"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16580"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788602"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5931"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185994"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/360"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29659-3_2"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3418487"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412152"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3052769"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3568953"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498427"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449788"},{"key":"e_1_3_3_26_2","article-title":"Addressing confounding feature issue for causal recommendation","author":"He Xiangnan","year":"2022","unstructured":"Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, and Yongdong Zhang. 2022. Addressing confounding feature issue for causal recommendation. ACM Transactions on Information Systems (TOIS) (2022).","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_3_27_2","first-page":"1670","volume-title":"International Conference on Machine Learning","author":"Schnabel Tobias","year":"2016","unstructured":"Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In International Conference on Machine Learning. PMLR, 1670\u20131679."},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3470948"},{"key":"e_1_3_3_29_2","volume-title":"The Thirty-second International FLAIRS Conference","author":"Abdollahpouri Himan","year":"2019","unstructured":"Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In The Thirty-second International FLAIRS Conference."},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531959"},{"issue":"2","key":"e_1_3_3_31_2","first-page":"1","article-title":"A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation","volume":"41","author":"Wu Haolun","year":"2022","unstructured":"Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, and Xue Liu. 2022. A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation. ACM Transactions on Information Systems 41, 2 (2022), 1\u201329.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539354"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-016-0406-7"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2018.00070"},{"key":"e_1_3_3_35_2","first-page":"141","volume-title":"Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland","volume":"85","author":"Bradley Keith","year":"2001","unstructured":"Keith Bradley and Barry Smyth. 2001. Improving recommendation diversity. In Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, Vol. 85. Citeseer, 141\u2013152."},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/290941.291025"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/1454008.1454030"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/1835449.1835486"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348309"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.5555\/2540128.2540517"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973440.53"},{"key":"e_1_3_3_42_2","first-page":"3868","volume-title":"IJCAI","author":"Sha Chaofeng","year":"2016","unstructured":"Chaofeng Sha, Xiaowei Wu, and Junyu Niu. 2016. A framework for recommending relevant and diverse items. In IJCAI, Vol. 16. 3868\u20133874."},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959149"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052585"},{"key":"e_1_3_3_45_2","first-page":"5627","volume-title":"Proceedings of the 31st Advances in Neural Information Processing Systems","author":"Chen Laming","year":"2018","unstructured":"Laming Chen, Guoxin Zhang, and Hanning Zhou. 2018. Fast greedy MAP inference for determinantal point process to improve recommendation diversity. In Proceedings of the 31st Advances in Neural Information Processing Systems. 5627\u20135638."},{"key":"e_1_3_3_46_2","article-title":"Reinforcement learning based recommender systems: A survey","author":"Afsar M. Mehdi","year":"2021","unstructured":"M. Mehdi Afsar, Trafford Crump, and Behrouz Far. 2021. Reinforcement learning based recommender systems: A survey. ACM Computing Surveys (CSUR) (2021).","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441786"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939878"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110335"},{"key":"e_1_3_3_50_2","first-page":"1265","article-title":"An MDP-based recommender system","volume":"6","author":"Shani Guy","year":"2005","unstructured":"Guy Shani, David Heckerman, and Ronen I. Brafman. 2005. An MDP-based recommender system. Journal of Machine Learning Research 6, Sep. (2005), 1265\u20131295.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013312"},{"key":"e_1_3_3_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219886"},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290999"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3089941"},{"key":"e_1_3_3_55_2","doi-asserted-by":"crossref","unstructured":"Yingqiang Ge Shuchang Liu Ruoyuan Gao Yikun Xian Yunqi Li Xiangyu Zhao Changhua Pei Fei Sun Junfeng Ge Wenwu Ou and Yongfeng Zhang. 2021. Towards long-term fairness in recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 445\u2013453.","DOI":"10.1145\/3437963.3441824"},{"key":"e_1_3_3_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412252"},{"key":"e_1_3_3_57_2","volume-title":"RecSys Posters","author":"Kamishima Toshihiro","year":"2014","unstructured":"Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2014. Correcting popularity bias by enhancing recommendation neutrality. In RecSys Posters."},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357898"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.10"},{"key":"e_1_3_3_60_2","first-page":"1","volume-title":"2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","author":"Yalcin Emre","year":"2022","unstructured":"Emre Yalcin. 2022. PopHybrid: A novel item popularity-aware hybrid approach for long-tail recommendation. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 1\u20136."},{"key":"e_1_3_3_61_2","volume-title":"Discrete Choice Methods with Simulation","author":"Train Kenneth E.","year":"2009","unstructured":"Kenneth E. Train. 2009. Discrete Choice Methods with Simulation. Cambridge University Press."},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_3_64_2","volume-title":"The PageRank Citation Ranking: Bringing Order to The Web.","author":"Page Lawrence","year":"1999","unstructured":"Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to The Web.Technical Report. Stanford InfoLab."},{"issue":"8","key":"e_1_3_3_65_2","first-page":"9","article-title":"Personalized book recommendation based on user preferences and commodity features","volume":"1","author":"Yinxiu Hou","year":"2017","unstructured":"Hou Yinxiu, Li Weiqing, Wang Weijun, and Zhang Tingting. 2017. Personalized book recommendation based on user preferences and commodity features. Data Analysis and Knowledge Discovery 1, 8 (2017), 9\u201317.","journal-title":"Data Analysis and Knowledge Discovery"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240368"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.56"},{"key":"e_1_3_3_68_2","first-page":"255","volume-title":"Proceedings of the Fourth Connectionist Models Summer School","author":"Thrun Sebastian","year":"1993","unstructured":"Sebastian Thrun and Anton Schwartz. 1993. Issues in using function approximation for reinforcement learning. In Proceedings of the Fourth Connectionist Models Summer School. Hillsdale, NJ, 255\u2013263."},{"key":"e_1_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"e_1_3_3_70_2","first-page":"176","volume-title":"International Conference on Machine Learning","author":"Anschel Oron","year":"2017","unstructured":"Oron Anschel, Nir Baram, and Nahum Shimkin. 2017. Averaged-DQN: Variance reduction and stabilization for deep reinforcement learning. In International Conference on Machine Learning. PMLR, 176\u2013185."},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014902"},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557220"},{"key":"e_1_3_3_73_2","volume-title":"Introduction to Information Retrieval","author":"Sch\u00fctze Hinrich","year":"2008","unstructured":"Hinrich Sch\u00fctze, Christopher D. Manning, and Prabhakar Raghavan. 2008. Introduction to Information Retrieval. Vol. 39. Cambridge University Press Cambridge."},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_3_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/1060745.1060754"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/1454008.1454038"},{"key":"e_1_3_3_77_2","first-page":"452","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI)","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). 452\u2013461."},{"key":"e_1_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_3_79_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367243.3367359"},{"key":"e_1_3_3_80_2","unstructured":"Wayne Xin Zhao Shanlei Mu Yupeng Hou Zihan Lin Yushuo Chen Xingyu Pan Kaiyuan Li Yujie Lu Hui Wang Changxin Tian Yingqian Min Zhichao Feng Xinyan Fan Xu Chen Pengfei Wang Wendi Ji and Yaliang Li. 2021. RecBole: Towards a unified comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4653\u20134664."},{"key":"e_1_3_3_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_3_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240374"},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.5555\/2786232.2786243"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3618107","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3618107","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:58Z","timestamp":1750178278000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3618107"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,8]]},"references-count":82,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3,31]]}},"alternative-id":["10.1145\/3618107"],"URL":"https:\/\/doi.org\/10.1145\/3618107","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,8]]},"assertion":[{"value":"2023-01-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}