Littérature scientifique sur le sujet « Large Scale Recommendation »
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Articles de revues sur le sujet "Large Scale Recommendation"
Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa et Ankur Narang. « Large Scale Multilingual Sticker Recommendation In Messaging Apps ». AI Magazine 42, no 4 (12 janvier 2022) : 16–28. http://dx.doi.org/10.1609/aimag.v42i4.15098.
Texte intégralZhou, Wang, Yongluan Zhou, Jianping Li et Muhammad Hammad Memon. « LsRec : Large-scale social recommendation with online update ». Expert Systems with Applications 162 (décembre 2020) : 113739. http://dx.doi.org/10.1016/j.eswa.2020.113739.
Texte intégralSakhi, Otmane, David Rohde et Alexandre Gilotte. « Fast Offline Policy Optimization for Large Scale Recommendation ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 8 (26 juin 2023) : 9686–94. http://dx.doi.org/10.1609/aaai.v37i8.26158.
Texte intégralLaddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa et Ankur Narang. « Large Scale Multilingual Sticker Recommendation In Messaging Apps ». AI Magazine 42, no 4 (18 janvier 2022) : 16–28. http://dx.doi.org/10.1609/aaai.12023.
Texte intégralLiu, Yang, Cheng Lyu, Zhiyuan Liu et Jinde Cao. « Exploring a large-scale multi-modal transportation recommendation system ». Transportation Research Part C : Emerging Technologies 126 (mai 2021) : 103070. http://dx.doi.org/10.1016/j.trc.2021.103070.
Texte intégralE, HaiHong, JianFeng WANG, MeiNa SONG, Qiang BI et YingYi LIU. « Incremental weighted bipartite algorithm for large-scale recommendation systems ». TURKISH JOURNAL OF ELECTRICAL ENGINEERING & ; COMPUTER SCIENCES 24 (2016) : 448–63. http://dx.doi.org/10.3906/elk-1307-91.
Texte intégralChen, Haokun, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang et Yong Yu. « Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 3312–20. http://dx.doi.org/10.1609/aaai.v33i01.33013312.
Texte intégralHASHIMOTO, T. « Recommendation for Large Scale Intervention Study on Industrial Population ». Sangyo Igaku 34, no 4 (1992) : 309. http://dx.doi.org/10.1539/joh1959.34.309.
Texte intégralKhan, Muhammad Usman Shahid, Osman Khalid, Ying Huang, Rajiv Ranjan, Fan Zhang, Junwei Cao, Bharadwaj Veeravalli, Samee U. Khan, Keqin Li et Albert Y. Zomaya. « MacroServ : A Route Recommendation Service for Large-Scale Evacuations ». IEEE Transactions on Services Computing 10, no 4 (1 juillet 2017) : 589–602. http://dx.doi.org/10.1109/tsc.2015.2497241.
Texte intégralBathla, Gourav, Himanshu Aggarwal et Rinkle Rani. « Scalable Recommendation Using Large Scale Graph Partitioning With Pregel and Giraph ». International Journal of Cognitive Informatics and Natural Intelligence 14, no 4 (octobre 2020) : 42–61. http://dx.doi.org/10.4018/ijcini.2020100103.
Texte intégralThèses sur le sujet "Large Scale Recommendation"
Nilsen, John Eirik Bjørhovde. « Large-Scale User Click Analysis in News Recommendation ». Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23004.
Texte intégralLarsson, Carl-Johan. « Movie Recommendation System Using Large Scale Graph-Processing ». Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.
Texte intégralSakhi, Otmane. « Offline Contextual Bandit : Theory and Large Scale Applications ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Texte intégralThis thesis presents contributions to the problem of learning from logged interactions using the offline contextual bandit framework. We are interested in two related topics: (1) offline policy learning with performance certificates, and (2) fast and efficient policy learning applied to large scale, real world recommendation. For (1), we first leverage results from the distributionally robust optimisation framework to construct asymptotic, variance-sensitive bounds to evaluate policies' performances. These bounds lead to new, more practical learning objectives thanks to their composite nature and straightforward calibration. We then analyse the problem from the PAC-Bayesian perspective, and provide tighter, non-asymptotic bounds on the performance of policies. Our results motivate new strategies, that offer performance certificates before deploying the policies online. The newly derived strategies rely on composite learning objectives that do not require additional tuning. For (2), we first propose a hierarchical Bayesian model, that combines different signals, to efficiently estimate the quality of recommendation. We provide proper computational tools to scale the inference to real world problems, and demonstrate empirically the benefits of the approach in multiple scenarios. We then address the question of accelerating common policy optimisation approaches, particularly focusing on recommendation problems with catalogues of millions of items. We derive optimisation routines, based on new gradient approximations, computed in logarithmic time with respect to the catalogue size. Our approach improves on common, linear time gradient computations, yielding fast optimisation with no loss on the quality of the learned policies
Safran, Mejdl Sultan. « EFFICIENT LEARNING-BASED RECOMMENDATION ALGORITHMS FOR TOP-N TASKS AND TOP-N WORKERS IN LARGE-SCALE CROWDSOURCING SYSTEMS ». OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1511.
Texte intégralYang, Dingqi. « Understanding human dynamics from large-scale location-centric social media data : analysis and applications ». Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0002/document.
Texte intégralHuman dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures
Yang, Dingqi. « Understanding human dynamics from large-scale location-centric social media data : analysis and applications ». Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0002.
Texte intégralHuman dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures
Östlin, Erik. « On Radio Wave Propagation Measurements and Modelling for Cellular Mobile Radio Networks ». Doctoral thesis, Karlskrona : Blekinge Institute of Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00443.
Texte intégralArrascue, Ayala Victor Anthony [Verfasser], et Georg [Akademischer Betreuer] Lausen. « Towards an effective consumption of large-scale knowledge graphs for recommendations ». Freiburg : Universität, 2020. http://d-nb.info/1223366189/34.
Texte intégralArdekani, Kamyar. « Feature Recommender : a large-scale in-situ study of proactive software feature recommendations ». Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59761.
Texte intégralScience, Faculty of
Computer Science, Department of
Graduate
Richardson, James Rutherford. « Accommodating existing settlements in large scale development : recommendations for Sha Tin New Town Hong Kong ». Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/69533.
Texte intégralThesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture; and, (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1981.
Bibliography: leaves 173-176.
by James Rutherford Richardson, IV.
Livres sur le sujet "Large Scale Recommendation"
Mellina, Eric. Overview of large-scale ecological experimental designs and recommendations for the British Columbia Watershed Restoration Program. Vancouver, B.C : Ministry of Environment, Lands, and Parks, 1995.
Trouver le texte intégralLukanin, Alleksandr. Cleaning of gas and air emissions. ru : INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1070340.
Texte intégralBerman, Amy, Edward Haertel et James Pellegrino, dir. Comparability of Large-Scale Educational Assessments : Issues and Recommendations. National Academy of Education, 2020. http://dx.doi.org/10.31094/2020/1.
Texte intégralHolden, Melanie A., Martin J. Thomas et Krysia S. Dziedzic. Miscellaneous physical therapies. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0026.
Texte intégralCawthon, Stephanie W. Large-Scale Survey Design in Deaf Education Research. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190455651.003.0009.
Texte intégralRecommendations for ground effects research for V/STOL and STOL aircraft and associated equipment for large scale testing. Moffett Field, Calif : National Aeronautics and Space Administration, Ames Research Center, 1986.
Trouver le texte intégralCook, Harry, et Michael Newson. Yemeni Irregular Migrants in the Kingdom of Saudi Arabia and the Implications of Large Scale Return. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190608873.003.0007.
Texte intégralKornell, Nate, et Bridgid Finn. Self-Regulated Learning. Sous la direction de John Dunlosky et Sarah (Uma) K. Tauber. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199336746.013.23.
Texte intégralBellosta-López, Pablo, Priscila de Brito Silva, Palle S. Jensen, Morten S. Hoegh, Thorvaldur S. Palsson, Steffan Wittrup Mc Phee Christensen, Julia Blasco-Abadía et al. Recommendations for implementation of the topic musculoskeletal disorders in the occupational health and safety postgraduate programmes at European Universities. Prevent4Work, 2021. http://dx.doi.org/10.54391/123456789/672.
Texte intégralDennis, Faber, et Vermunt Niels, dir. Bank Failure : Lessons from Lehman Brothers. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198755371.001.0001.
Texte intégralChapitres de livres sur le sujet "Large Scale Recommendation"
Abbasi, Rabeeh, Marcin Grzegorzek et Steffen Staab. « Large Scale Tag Recommendation Using Different Image Representations ». Dans Semantic Multimedia, 65–76. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10543-2_8.
Texte intégralBenouaret, Idir, et Dominique Lenne. « A Package-to-Group Recommendation Framework ». Dans Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX, 43–66. Berlin, Heidelberg : Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58415-6_2.
Texte intégralProkofyev, Roman, Alexey Boyarsky, Oleg Ruchayskiy, Karl Aberer, Gianluca Demartini et Philippe Cudré-Mauroux. « Tag Recommendation for Large-Scale Ontology-Based Information Systems ». Dans The Semantic Web – ISWC 2012, 325–36. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35173-0_22.
Texte intégralDraidi, Fady, Esther Pacitti et Bettina Kemme. « P2Prec : A P2P Recommendation System for Large-Scale Data Sharing ». Dans Transactions on Large-Scale Data- and Knowledge-Centered Systems III, 87–116. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23074-5_4.
Texte intégralYu, Ruiguo, Jianrong Wang, Tianyi Xu, Jie Gao, Kunyu Cao et Mei Yu. « Communities Mining and Recommendation for Large-Scale Mobile Social Networks ». Dans Wireless Algorithms, Systems, and Applications, 266–77. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60033-8_24.
Texte intégralChen, Ming, Chunying Li, Jiwei Liu, Dejie Meng et Yong Tang. « Scholar Recommendation Model in Large Scale Academic Social Networking Platform ». Dans Human Centered Computing, 453–64. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74521-3_48.
Texte intégralFu, Huazheng, Kang Chen et Jianbing Ding. « An Empirical Study of a Large Scale Online Recommendation System ». Dans Web Technologies and Applications, 15–25. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28121-6_2.
Texte intégralMaurya, Chandresh Kumar, Seemandhar Jain et Vishal Thakre. « Large-Scale Contact Tracing, Hotspot Detection, and Safe Route Recommendation ». Dans Big Data Analytics, 163–82. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_13.
Texte intégralServajean, Maximilien, Esther Pacitti, Miguel Liroz-Gistau, Sihem Amer-Yahia et Amr El Abbadi. « Increasing Coverage in Distributed Search and Recommendation with Profile Diversity ». Dans Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII, 115–44. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48567-5_4.
Texte intégralShi, Zhenkui. « Privacy-Assured Large-Scale Navigation from Encrypted Approximate Shortest Path Recommendation ». Dans Communications in Computer and Information Science, 195–211. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8890-2_14.
Texte intégralActes de conférences sur le sujet "Large Scale Recommendation"
Ji, Houye, Junxiong Zhu, Chuan Shi, Xiao Wang, Bai Wang, Chaoyu Zhang, Zixuan Zhu, Feng Zhang et Yanghua Li. « Large-scale Comb-K Recommendation ». Dans WWW '21 : The Web Conference 2021. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3442381.3449924.
Texte intégralZhang, Xindong, Chenguang Zhu, Yi Li, Jianmei Guo, Lihua Liu et Haobo Gu. « Large-scale patch recommendation at Alibaba ». Dans ICSE '20 : 42nd International Conference on Software Engineering. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3377812.3390902.
Texte intégralSwezey, Robin M. E., et Bruno Charron. « Large-scale recommendation for portfolio optimization ». Dans RecSys '18 : Twelfth ACM Conference on Recommender Systems. New York, NY, USA : ACM, 2018. http://dx.doi.org/10.1145/3240323.3240386.
Texte intégralLee, Joonseok, et Sami Abu-El-Haija. « Large-Scale Content-Only Video Recommendation ». Dans 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2017. http://dx.doi.org/10.1109/iccvw.2017.121.
Texte intégralChen, Chaochao, Xinxing Yang, Li Wang, Jun Zhou et Xiaolong Li. « Large scale app recommendation in Ant Financial ». Dans 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258524.
Texte intégralChamberlain, Benjamin P., Emanuele Rossi, Dan Shiebler, Suvash Sedhain et Michael M. Bronstein. « Tuning Word2vec for Large Scale Recommendation Systems ». Dans RecSys '20 : Fourteenth ACM Conference on Recommender Systems. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3383313.3418486.
Texte intégralJoglekar, Manas R., Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu et Quoc V. Le. « Neural Input Search for Large Scale Recommendation Models ». Dans KDD '20 : The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3394486.3403288.
Texte intégralLerallut, Romain, Diane Gasselin et Nicolas Le Roux. « Large-Scale Real-Time Product Recommendation at Criteo ». Dans RecSys '15 : Ninth ACM Conference on Recommender Systems. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2792838.2799498.
Texte intégralYang, Longqi, Tobias Schnabel, Paul N. Bennett et Susan Dumais. « Local Factor Models for Large-Scale Inductive Recommendation ». Dans RecSys '21 : Fifteenth ACM Conference on Recommender Systems. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3460231.3474276.
Texte intégralQian, Shiyou, Yanmin Zhu et Minglu Li. « Smart recommendation by mining large-scale GPS traces ». Dans 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012. http://dx.doi.org/10.1109/wcnc.2012.6214371.
Texte intégralRapports d'organisations sur le sujet "Large Scale Recommendation"
Spane, Frank A. Large-Scale Pumping Test Recommendations for the 200-ZP-1 Operable Unit. Office of Scientific and Technical Information (OSTI), septembre 2010. http://dx.doi.org/10.2172/989035.
Texte intégralAlbornoz, Facundo, Guillermo Cruces et María Lombardi. Trusting Covid-19 recommendations : The role of experts, markets and governments. Inter-American Development Bank, août 2023. http://dx.doi.org/10.18235/0005097.
Texte intégralFord, Adam T., Marcel Huijser et Anthony P. Clevenger. Long-term responses of an ecological community to highway mitigation measures. Nevada Department of Transportation, juin 2022. http://dx.doi.org/10.15788/ndot2022.06.
Texte intégralBarba, Ricardo Carlos, Sourav Majumder, Palak Rawal et Saswati Ghosh Belliappa. Resettling Urban Populations : Learning from the Graduation Approach in India. Asian Development Bank, mai 2023. http://dx.doi.org/10.22617/wps230201-2.
Texte intégralHinrichs, Claudia, et Judith Hauck. Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. OceanNets, juin 2022. http://dx.doi.org/10.3289/oceannets_d4.4.
Texte intégralMuldavin, Esteban, Yvonne Chauvin, Teri Neville, Hannah Varani, Jacqueline Smith, Paul Neville et Tani Hubbard. A vegetation classi?cation and map : Guadalupe Mountains National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302855.
Texte intégralMarcos Morezuelas, Paloma. Gender and Renewable Energy : Wind, Solar, Geothermal and Hydroelectric Energy. Inter-American Development Bank, novembre 2014. http://dx.doi.org/10.18235/0003068.
Texte intégralZhai, Yuhui, et Yanfeng Ouyang. Effects of Nontraditional Messages in Dynamic Message Signs on Improving Safety, Compliance, and Avoiding Distraction. Illinois Center for Transportation, mai 2024. http://dx.doi.org/10.36501/0197-9191/24-014.
Texte intégralDunlop, Steven R., Satish Ukkusuri, Dutt J. Thakkar, Shagun Mittal, Utkarsh Patil, Jainam Gala et Thomas Brady. Economic Effect of Active Transportation Features and the Association Between the Healthcare Industry and Transportation. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317655.
Texte intégralBuesseler, Buessele, Daniele Bianchi, Fei Chai, Jay T. Cullen, Margaret Estapa, Nicholas Hawco, Seth John et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, octobre 2023. http://dx.doi.org/10.1575/1912/67120.
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