Relevant bibliographies by topics / Recommender Systems (RecSys)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Recommender Systems (RecSys)'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Recommender Systems (RecSys)"

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Zheng, Yong, Hanna Hauptmann, and Guibing Guo. "ACM Conference on Recommender Systems September 18--22, 2023 at Singapore." ACM SIGWEB Newsletter 2023, Summer (2023): 1–4. http://dx.doi.org/10.1145/3609429.3609431.

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ACM Conference on Recommender Systems (RecSys) is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. RecSys 2023 will be held in Singapore and will bring together the major international research groups working on recommender systems, along with many of the world's leading companies active in e-commerce and other adjacent domains. It is a SIGCHI-sponsored conference, that will attract also several leading industry sponsors. The proceedings will be published by ACM as part of the ACM Digital Library.
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Sun, Aixin. "Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems." ACM SIGWEB Newsletter 2024, Spring (2024): 1–11. http://dx.doi.org/10.1145/3663752.3663756.

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Recommender Systems (RecSys) have become indispensable in numerous applications, profoundly influencing our everyday experiences. Despite their practical significance, academic research in RecSys often abstracts the formulation of research tasks from real-world contexts, aiming for a clean problem formulation and more generalizable findings. However, it is observed that there is a lack of collective understanding in RecSys academic research. The root of this issue may lie in the simplification of research task definitions, and an overemphasis on modeling the decision outcomes rather than the d
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An, Yiquan, Yingxin Tan, Xi Sun, and Giovannipaolo Ferrari. "Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications." IECE Transactions on Sensing, Communication, and Control 1, no. 1 (2024): 30–51. http://dx.doi.org/10.62762/tscc.2024.898503.

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The proliferation of Recommender Systems (RecSys), driven by their expanding application domains, explosive data growth, and exponential advancements in computing capabilities, has cultivated a dynamic and evolving research landscape. This paper comprehensively reviews the foundational concepts, methodologies, and challenges associated with RecSys from technological and social scientific lenses. Initially, it categorizes personalized RecSys technical solutions into five paradigms: collaborative filtering, scenario-aware, knowledge & data co-driven approaches, large language models, and hyb
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Graus, David, Toine Bogers, Mesut Kaya, Francisco Gutiérrez, and Katrien Verbert. "Report on the 1st workshop on recommender systems for human resources (RecSys in HR 2021) at RecSys 2021." ACM SIGIR Forum 55, no. 2 (2021): 1–14. http://dx.doi.org/10.1145/3527546.3527567.

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Recommender systems are increasingly used in more high risk application domains, including in the domain of Human Resources (HR). These recommender systems help end-users find relevant vacancies out of an abundant overload of available vacancies, but also support other important objectives such as job mobility. Despite the use in industry applications, there are several research challenges associated to such objectives that have not yet been addressed in detail in this context, such as supporting end-users to steer the recommendation process with input and feedback and increasing diversity of
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Wang, Cheng, Mathias Niepert, and Hui Li. "RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems." IEEE Transactions on Neural Networks and Learning Systems 31, no. 8 (2020): 2731–40. http://dx.doi.org/10.1109/tnnls.2019.2907430.

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Said, Alan. "A Short History of the RecSys Challenge." AI Magazine 37, no. 4 (2017): 102–4. http://dx.doi.org/10.1609/aimag.v37i4.2693.

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The RecSys Challenge is a yearly recurring competition focusing on creating the best performing recommendation approach for a specific scenario. Over the years, the competition has drawn many participants from industry and academia, and has become an key part of the ACM Conference on Recommender Systems series. This article presents a brief historical overview of the RecSys Challenge from its inception in 2010 until the seventh iteration in 2016.
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Kristensen, Alexander, and Der Berg Charlotte Van. "Enhancing Energy Sector E-Commerce Data Storage through Distributed File Systems and Cloud Solutions." Academic Journal of Sociology and Management 2, no. 4 (2024): 35–40. https://doi.org/10.5281/zenodo.12747432.

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Recommender Systems (RecSys) play a crucial role in managing information overload and enhancing user satisfaction across various digital platforms, including e-commerce and entertainment. Evolving from traditional models to Deep Neural Networks (DNNs) and more recently, Large Language Models (LLMs), these systems leverage sophisticated algorithms to analyze user behaviors and preferences. LLMs, such as GPT-4, are trained on extensive datasets to comprehend and generate natural language, significantly advancing their ability to deliver personalized recommendations. This tutorial explores the tr
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Said, Alan, Eva Zangerle, and Christine Bauer. "Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023." ACM SIGIR Forum 57, no. 2 (2023): 1–4. http://dx.doi.org/10.1145/3642979.3643000.

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Evaluation is a central step when developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2023 workshop, held as part of the 17th ACM Conference on Recommender Systems (RecSys 2023), served as a forum where researchers from both academia and industry critically reflected on the evaluation of recommender systems. The goal of the PERSPECTIVES workshop series is to capture the current state of evaluation from different perspectives and discuss the different targets that recommender systems evaluation should strive for. In the third edition of the workshop, we discussed problem
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Bogers, Toine, Marijn Koolen, Cataldo Musto, Pasquale Lops, and Giovanni Semeraro. "Report on RecSys 2016Workshop on New Trends in Content-Based Recommender Systems." ACM SIGIR Forum 51, no. 1 (2017): 45–51. http://dx.doi.org/10.1145/3130332.3130341.

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Bunnell, Lawrence, Kweku-Muata Osei-Bryson, and Victoria Y. Yoon. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers." Information Systems Frontiers 22, no. 6 (2019): 1377–418. http://dx.doi.org/10.1007/s10796-019-09935-9.

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Dissertations / Theses on the topic "Recommender Systems (RecSys)"

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Shevtsov, Stepan. "Developing a recommender mechanism for supporting mobile content reuse." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-40778.

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Nowadays people got used to short text answers, likes and shares. Anyone can feel it by entering popular forums or social networks. Modern platforms such as Twitter or Facebook contribute to this situation with their symbols per message limitations. The quality of content produced in such conditions is not high. According to Knight and Burn (2005): “The rapid growth of the Internet and the lack of enforceable standards regarding the information it contains has led to numerous information quality problems.” The possible solution to this problem is called mobile digital storytelling. It replaces
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Books on the topic "Recommender Systems (RecSys)"

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ACM, Conference on Recommender Systems (1st 2007 Minneapolis Minn ). RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems : Minneapolis, MN, USA, October 19-20, 2007. Association for Computing Machinery, 2007.

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ACM, Conference on Recommender Systems (1st 2007 Minneapolis Minn ). RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems : Minneapolis, MN, USA, October 19-20, 2007. Association for Computing Machinery, 2007.

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SIGCHI (Group : U.S.), ed. RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems : Minneapolis, MN, USA, October 19-20, 2007. Association for Computing Machinery, 2007.

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Recsys Conference Committee. RecSys 15: Recommender Systems. Association for Computing Machinery, 2015.

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Recsys 14 Conference Committee. RecSys'14: Recommender Systems. Association for Computing Machinery, 2014.

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RecSys '14: Eighth ACM Conference on Recommender Systems. Association for Computing Machinery, 2014.

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RecSys '17: Eleventh ACM Conference on Recommender Systems. Association for Computing Machinery, 2017.

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RecSys '17: Eleventh ACM Conference on Recommender Systems. ACM, 2018.

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RecSys '14: Eighth ACM Conference on Recommender Systems. Association for Computing Machinery, 2014.

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RecSys '12: Sixth ACM Conference on Recommender Systems CD-ROM. Association for Computing Machinery, 2012.

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Book chapters on the topic "Recommender Systems (RecSys)"

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Tobias, Joseph D., and Gregory A. Hollman. "Credentialing and Recredentialing for Pediatric Sedation." In The Pediatric Procedural Sedation Handbook, edited by Cheryl K. Gooden, Lia H. Lowrie, and Benjamin F. Jackson. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190659110.003.0059.

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Given the complexity of the knowledge and skills required for the safe conduct of pediatric sedation, compounded by the diversity of sedation practitioners, a formal process for training and credentialing is recommended. Credentialing and recredentialing are the processes by which hospitals collect and review a practitioner’s qualifications as part of the initial step in granting clinical privileges. Regulatory boards mandate that each institution have a system in place that defines qualification requirements and approval of privileges for clinical practices such as procedural sedation. The cr
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Deshpande, Ravikant M., Bharati V. Patle, and Ranjana D. Bhoskar. "Planning and Implementation of Cloud Computing in NIT's in India." In Cloud Technology. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6539-2.ch084.

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Cloud computing and Information Network both are emerging facets in the field of Information Science (IS). Cloud computing has potential to bring another wave of changes to organizations. Overall, planning and implementation of cloud computing will be beneficial in the terms of library level as well as institutional level to manage by cloud. In this chapter the authors present RECs/NITs resources to move on for managing cloud computing. Making the decision to use cloud-based services means balancing the elements of cost, risk, and benefit to decide whether those services advance the mission of
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Deshpande, Ravikant M., Bharati V. Patle, and Ranjana D. Bhoskar. "Planning and Implementation of Cloud Computing in NIT’s in India: Special Reference to VNIT." In Advances in Library and Information Science. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4631-5.ch006.

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Cloud computing and Information Network both are emerging facets in the field of Information Science (IS). Cloud computing has potential to bring another wave of changes to organizations. Overall, planning and implementation of cloud computing will be beneficial in the terms of library level as well as institutional level to manage by cloud. In this chapter the authors present RECs/NITs resources to move on for managing cloud computing. Making the decision to use cloud-based services means balancing the elements of cost, risk, and benefit to decide whether those services advance the mission of
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Deshpande, Ravikant M., Bharati V. Patle, and Ranjana D. Bhoskar. "Planning and Implementation of Cloud Computing in NIT's in India: Special Reference to VNIT." In Web-Based Services. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9466-8.ch009.

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Cloud computing and Information Network both are emerging facets in the field of Information Science (IS). Cloud computing has potential to bring another wave of changes to organizations. Overall, planning and implementation of cloud computing will be beneficial in the terms of library level as well as institutional level to manage by cloud. In this chapter the authors present RECs/NITs resources to move on for managing cloud computing. Making the decision to use cloud-based services means balancing the elements of cost, risk, and benefit to decide whether those services advance the mission of
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Fluke, John D., Mónica López López, Rami Benbenishty, Erik J. Knorth, and Donald J. Baumann. "Advancing the Field of Decision-Making and Judgment in Child Welfare and Protection." In Decision-Making and Judgment in Child Welfare and Protection. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190059538.003.0014.

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Our knowledge of decision-making in child welfare has evolved concurrent with the recognition of variability in the rates at which children and families experience no involvement to deeper involvement in the system. This chapter recaps the challenges to identifying systematic causes for variability in decisions and what can be learned about them. While research indicates that emphasis on both formal and informal assessment is warranted, errors and mistakes can happen at all stages of the assessment and decision processes due to a range of system and human factors. The Decision-Making Ecology (
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Conference papers on the topic "Recommender Systems (RecSys)"

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Pacuk, Andrzej, Piotr Sankowski, Karol Węgrzycki, Adam Witkowski, and Piotr Wygocki. "RecSys Challenge 2016." In the Recommender Systems Challenge. ACM Press, 2016. http://dx.doi.org/10.1145/2987538.2987544.

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Pálovics, Róbert, Frederick Ayala-Gómez, Balázs Csikota, et al. "RecSys Challenge 2014." In the 2014 Recommender Systems Challenge. ACM Press, 2014. http://dx.doi.org/10.1145/2668067.2668071.

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Honrado, Jose Ignacio, Oscar Huarte, Cesar Jimenez, et al. "Jobandtalent at RecSys Challenge 2016." In the Recommender Systems Challenge. ACM Press, 2016. http://dx.doi.org/10.1145/2987538.2987547.

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Garcin, Florent, and Boi Faltings. "PEN RecSys." In RecSys '13: Seventh ACM Conference on Recommender Systems. ACM, 2013. http://dx.doi.org/10.1145/2507157.2508218.

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Singh, Pallavi, Gyanit Singh, and Anurag Bhardwaj. "Ranking approach to RecSys Challenge." In the 2014 Recommender Systems Challenge. ACM Press, 2014. http://dx.doi.org/10.1145/2668067.2668069.

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Garcin, Florent, and Boi Faltings. "PEN recsys." In the 2013 International News Recommender Systems Workshop and Challenge. ACM Press, 2013. http://dx.doi.org/10.1145/2516641.2516642.

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Romov, Peter, and Evgeny Sokolov. "RecSys Challenge 2015." In RecSys '15: Ninth ACM Conference on Recommender Systems. ACM, 2015. http://dx.doi.org/10.1145/2813448.2813510.

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Knees, Peter, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard-Paul Leyson, and Philipp Monreal. "RecSys challenge 2019." In RecSys '19: Thirteenth ACM Conference on Recommender Systems. ACM, 2019. http://dx.doi.org/10.1145/3298689.3346974.

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Chen, Ching-Wei, Paul Lamere, Markus Schedl, and Hamed Zamani. "Recsys challenge 2018." In RecSys '18: Twelfth ACM Conference on Recommender Systems. ACM, 2018. http://dx.doi.org/10.1145/3240323.3240342.

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Abel, Fabian, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. "RecSys Challenge 2017." In RecSys '17: Eleventh ACM Conference on Recommender Systems. ACM, 2017. http://dx.doi.org/10.1145/3109859.3109954.

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