{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:46:53Z","timestamp":1750308413173,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3481907","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:53:43Z","timestamp":1636991623000},"page":"4234-4242","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Crawler Detection in Location-Based Services Using Attributed Action Net"],"prefix":"10.1145","author":[{"given":"Wei","family":"Xia","sequence":"first","affiliation":[{"name":"Alibaba Group, Shanghai, China"}]},{"given":"Fei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Alibaba Group, Shanghai, China"}]},{"given":"Haishuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Fairfield University, Fairfield, CT, USA"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangzhou University, Guangzhou, China"}]},{"given":"Anhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Shanghai, China"}]},{"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"unstructured":"Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell etal 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020).  Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020).","key":"e_1_3_2_1_1_1"},{"volume-title":"An Overview of Web Robots Detection Techniques. In 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). IEEE, 1--6.","year":"2020","author":"Chen Hanlin","key":"e_1_3_2_1_2_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1145\/3326937.3341261"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1145\/2939672.2939785"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1145\/3159652.3159668"},{"volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","year":"2018","author":"Devlin Jacob","key":"e_1_3_2_1_6_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1007\/s10618-020-00710-y"},{"volume-title":"Greedy function approximation: a gradient boosting machine. Annals of statistics","year":"2001","author":"Friedman Jerome H","key":"e_1_3_2_1_8_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1109\/DSAA.2017.13"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1162\/neco.1997.9.8.1735"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.5555\/3294996.3295074"},{"volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","year":"2014","author":"Kingma Diederik P","key":"e_1_3_2_1_12_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1109\/ICTAI.2018.00150"},{"volume-title":"Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942","year":"2019","author":"Lan Zhenzhong","key":"e_1_3_2_1_14_1"},{"key":"e_1_3_2_1_15_1","volume-title":"Nature","volume":"521","author":"Lecun Yann","year":"2015"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1145\/3394486.3403354"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1145\/3219819.3219950"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1145\/3394486.3403091"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.23919\/FRUCT.2017.8071322"},{"volume-title":"Improving language understanding by generative pre-training. OpenAI blog","year":"2018","author":"Radford Alec","key":"e_1_3_2_1_20_1"},{"volume-title":"Language models are unsupervised multitask learners. OpenAI blog","year":"2019","author":"Radford Alec","key":"e_1_3_2_1_21_1"},{"unstructured":"Javad Rajabnia and Majid Vafaei Jahan. 2020. Web robot detection with fuzzy inference system based on NNGE (non-nested generalized exemplar). https:\/\/www.academia.edu\/8753500\/Web_Robot_Detection_With_Fuzzy_Inference_System_Based_On_NNGE.  Javad Rajabnia and Majid Vafaei Jahan. 2020. Web robot detection with fuzzy inference system based on NNGE (non-nested generalized exemplar). https:\/\/www.academia.edu\/8753500\/Web_Robot_Detection_With_Fuzzy_Inference_System_Based_On_NNGE.","key":"e_1_3_2_1_22_1"},{"volume-title":"Joong Soo Han, and Eul Gyu Im","year":"2018","author":"Ro Inwoo","key":"e_1_3_2_1_23_1"},{"volume-title":"2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). IEEE, 365--370","key":"e_1_3_2_1_24_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1145\/3357384.3357895"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.9790\/0661-16160105"},{"volume-title":"Attention is all you need. arXiv preprint arXiv:1706.03762","year":"2017","author":"Vaswani Ashish","key":"e_1_3_2_1_27_1"},{"doi-asserted-by":"crossref","unstructured":"Haishuai Wang Paul Avillach etal 2021. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR medical informatics Vol. 9 4 (2021) e24754.  Haishuai Wang Paul Avillach et al. 2021. Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning. JMIR medical informatics Vol. 9 4 (2021) e24754.","key":"e_1_3_2_1_28_1","DOI":"10.2196\/24754"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_29_1","DOI":"10.1109\/TCBB.2018.2827029"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.1145\/3394486.3403307"},{"volume-title":"Attributed Sequence Embedding. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 1723--1728","year":"2019","author":"Zhuang Zhongfang","key":"e_1_3_2_1_31_1"}],"event":{"sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"acronym":"CIKM '21","name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia"},"container-title":["Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3481907","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3481907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:11Z","timestamp":1750268951000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3481907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":31,"alternative-id":["10.1145\/3459637.3481907","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3481907","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}