{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T01:45:35Z","timestamp":1775094335878,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"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":[[2023,7,19]]},"DOI":"10.1145\/3539618.3591749","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:22:59Z","timestamp":1689726179000},"page":"1014-1022","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Personalized Retrieval over Millions of Items"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5167-4353","authenticated-orcid":false,"given":"Hemanth","family":"Vemuri","sequence":"first","affiliation":[{"name":"Microsoft, Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0423-1053","authenticated-orcid":false,"given":"Sheshansh","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9173-026X","authenticated-orcid":false,"given":"Shivam","family":"Mittal","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bengaluru, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6057-4351","authenticated-orcid":false,"given":"Deepak","family":"Saini","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3518-7667","authenticated-orcid":false,"given":"Akshay","family":"Soni","sequence":"additional","affiliation":[{"name":"Microsoft, Mountain View, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8854-0973","authenticated-orcid":false,"given":"Abhinav V.","family":"Sambasivan","sequence":"additional","affiliation":[{"name":"Microsoft, Mountain View, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6395-6024","authenticated-orcid":false,"given":"Wenhao","family":"Lu","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6828-0545","authenticated-orcid":false,"given":"Yajun","family":"Wang","sequence":"additional","affiliation":[{"name":"LinkedIn Corporation, Sunnyvale, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6857-0858","authenticated-orcid":false,"given":"Mehul","family":"Parsana","sequence":"additional","affiliation":[{"name":"Google, Mountain View, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2096-5267","authenticated-orcid":false,"given":"Purushottam","family":"Kar","sequence":"additional","affiliation":[{"name":"IIT Kanpur, Kanpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4516-6613","authenticated-orcid":false,"given":"Manik","family":"Varma","sequence":"additional","affiliation":[{"name":"Microsoft Research, Bengaluru, India"}]}],"member":"320","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mingxiao An Fangzhao Wu Chuhan Wu Kun Zhang Zheng Liu and Xing Xie. 2019. Neural news recommendation with long-and short-term user representations. In ACL."},{"key":"e_1_3_2_1_2_1","unstructured":"K. Bhatia K. Dahiya H. Jain A. Mittal Y. Prabhu and M. Varma. 2016. The Extreme Classification Repository: Multi-label Datasets & Code. http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html"},{"key":"e_1_3_2_1_3_1","unstructured":"T. Chen S. Kornblith M. Norouzi and G. Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_5_1","volume-title":"SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels. In ICML.","author":"Dahiya K.","unstructured":"K. Dahiya, A. Agarwal, D. Saini, K. Gururaj, J. Jiao, A. Singh, S. Agarwal, P. Kar, and M. Varma. 2021. SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels. In ICML."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570392"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0895479895290954"},{"key":"e_1_3_2_1_8_1","volume-title":"VSE: Improving Visual-Semantic Embeddings with Hard Negatives. In BMVC.","author":"Faghri F.","year":"2018","unstructured":"F. Faghri, D.-J. Fleet, J.-R. Kiros, and S. Fidler. 2018. VSE: Improving Visual-Semantic Embeddings with Hard Negatives. In BMVC."},{"key":"e_1_3_2_1_9_1","unstructured":"Shaohua Fan Junxiong Zhu Xiaotian Han Chuan Shi Linmei Hu Biyu Ma and Yongliang Li. 2019. Metapath-guided heterogeneous graph neural network for intent recommendation. In KDD."},{"key":"e_1_3_2_1_10_1","unstructured":"Jiafeng Guo Yixing Fan Qingyao Ai and W. Bruce Croft. 2016. A Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"P. S. Huang X. He J. Gao L. Deng A. Acero and L. Heck. 2013. Learning Deep Structured Semantic Models for Web Search using Clickthrough Data. In CIKM.","DOI":"10.1145\/2505515.2505665"},{"key":"e_1_3_2_1_12_1","unstructured":"Houyi Li Zhihong Chen Chenliang Li Rong Xiao Hongbo Deng Peng Zhang Yongchao Liu and Haihong Tang. 2021. Path-based Deep Network for Candidate Item Matching in Recommenders. In SIGIR."},{"key":"e_1_3_2_1_13_1","volume-title":"COT: Contextual Operating Tensor for Context-Aware Recommender Systems. In AAAI.","author":"Liu Qiang","year":"2015","unstructured":"Qiang Liu, Shu Wu, and Liang Wang. 2015. COT: Contextual Operating Tensor for Context-Aware Recommender Systems. In AAAI."},{"key":"e_1_3_2_1_14_1","volume-title":"Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates. In SIGIR.","author":"Liu Zheng","year":"2020","unstructured":"Zheng Liu, Jianxun Lian, Junhan Yang, Defu Lian, and Xing Xie. 2020. Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates. In SIGIR."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"W. Lu J. Jiao and R. Zhang. 2020. TwinBERT: Distilling Knowledge to Twin-Structured Compressed BERT Models for Large-Scale Retrieval. In CIKM.","DOI":"10.1145\/3340531.3412747"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"A. Y. Malkov and D. A. Yashunin. 2020. Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. TPAMI (2020).","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_3_2_1_17_1","unstructured":"Tharun Kumar Reddy Medini Qixuan Huang Yiqiu Wang Vijai Mohan and Anshumali Shrivastava. 2019. Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products. In NeurIPS."},{"key":"e_1_3_2_1_18_1","volume-title":"Foundations and Trends\u00ae in Information Retrieval","volume":"13","author":"Mitra Bhaskar","year":"2019","unstructured":"Bhaskar Mitra and Nick Craswell. 2019. An Introduction to Neural Information Retrieval. Foundations and Trends\u00ae in Information Retrieval, Vol. 13, 1 (2019), 1--126."},{"key":"e_1_3_2_1_19_1","volume-title":"DECAF: Deep Extreme Classification with Label Features. In WSDM.","author":"Mittal A.","year":"2021","unstructured":"A. Mittal, K. Dahiya, S. Agrawal, D. Saini, S. Agarwal, P. Kar, and M. Varma. 2021. DECAF: Deep Extreme Classification with Label Features. In WSDM."},{"key":"e_1_3_2_1_20_1","unstructured":"Jianmo Ni Jiacheng Li and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In EMNLP-IJCNLP."},{"key":"e_1_3_2_1_21_1","unstructured":"Yabo Ni Dan Ou Shichen Liu Xiang Li Wenwu Ou Anxiang Zeng and Luo Si. 2018. Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In KDD."},{"key":"e_1_3_2_1_22_1","unstructured":"Rodrigo Nogueira Wei Yang Kyunghyun Cho and Jimmy Lin. 2019. Multi-stage document ranking with BERT. arXiv:1910.14424."},{"key":"e_1_3_2_1_23_1","unstructured":"Aditya Pal Chantat Eksombatchai Yitong Zhou Bo Zhao Charles Rosenberg and Jure Leskovec. 2020. PinnerSage: multi-modal user embedding framework for recommendations at pinterest. In KDD."},{"key":"e_1_3_2_1_24_1","volume-title":"Kushal Dave, Jian Jiao, Amit Singh, Ruofei Zhang, and Manik Varma.","author":"Saini Deepak","year":"2021","unstructured":"Deepak Saini, Arnav Kumar Jain, Kushal Dave, Jian Jiao, Amit Singh, Ruofei Zhang, and Manik Varma. 2021. GalaXC: Graph neural networks with labelwise attention for extreme classification. In WWW."},{"key":"e_1_3_2_1_25_1","unstructured":"V. Sanh L. Debut J. Chaumond and T. Wolf. 2019. DistilBERT a distilled version of BERT: smaller faster cheaper and lighter. arXiv:1910.01108."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In CIKM.","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_27_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NeurIPS."},{"key":"e_1_3_2_1_28_1","unstructured":"Tian Wang Yuri M Brovman and Sriganesh Madhvanath. 2021. Personalized Embedding-based e-Commerce Recommendations at eBay. arXiv:2102.06156."},{"key":"e_1_3_2_1_29_1","volume-title":"Transformers: State-of-the-Art Natural Language Processing. In EMNLP: System Demonstrations.","author":"Wolf T.","year":"2020","unstructured":"T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In EMNLP: System Demonstrations."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330665"},{"key":"e_1_3_2_1_31_1","unstructured":"Chuhan Wu Fangzhao Wu Junxin Liu Shaojian He Yongfeng Huang and Xing Xie. 2019b. Neural demographic prediction using search query. In WSDM."},{"key":"e_1_3_2_1_32_1","unstructured":"L. Xiong C. Xiong Y. Li K.-F. Tang J. Liu P. Bennett J. Ahmed and A. Overwijk. 2021. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In ICLR."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Han Zhang Songlin Wang Kang Zhang Zhiling Tang Yunjiang Jiang Yun Xiao Weipeng Yan and Wen-Yun Yang. 2020. Towards personalized and semantic retrieval: An end-to-end solution for e-commerce search via embedding learning. In SIGIR.","DOI":"10.1145\/3397271.3401446"}],"event":{"name":"SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Taipei Taiwan","acronym":"SIGIR '23","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591749","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539618.3591749","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:01Z","timestamp":1750178821000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":33,"alternative-id":["10.1145\/3539618.3591749","10.1145\/3539618"],"URL":"https:\/\/doi.org\/10.1145\/3539618.3591749","relation":{},"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"2023-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}