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

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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2

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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3

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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6

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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8

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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9

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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10

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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11

Zhao, Xiangyu. "Adaptive and automated deep recommender systems." ACM SIGWEB Newsletter, Spring (April 2022): 1–4. http://dx.doi.org/10.1145/3533274.3533277.

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Dr. Xiangyu Zhao is an assistant professor of the school of data science at City University of Hong Kong (CityU). Prior to CityU, he completed his PhD (2021) at MSU under the advisory of Dr. Jiliang Tang, MS (2017) at USTC and BEng (2014) at UESTC. His current research interests include data mining and machine learning, especially (1) Personalization, Recommender System, Online Advertising, Search Engine, and Information Retrieval; (2) Urban Computing, Smart City, and GeoAI; (3) Deep Reinforcement Learning, AutoML, and Multimodal ML; and (4) AI for Social Computing, Finance, Education, Ecosyst

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12

Brusilovsky, Peter, Marco de Gemmis, Alexander Felfernig, et al. "Report on the 10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2023) at ACM RecSys 2023." ACM SIGIR Forum 57, no. 2 (2023): 1–6. http://dx.doi.org/10.1145/3642979.3642999.

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The 10th edition of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems was held as part of the 17th ACM Conference on Recommender Systems (RecSys), the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. The workshop was organized as a hybrid event: the physical session took place on September 18th at the venue of the main conference, Singapore, with the possibility for authors to present remotely. The IntRS workshop brings together an interdisciplinary community of researche

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13

Anelli, Vito Walter, Pierpaolo Basile, Toine Bogers, et al. "Report on the 3rd workshop of knowledge-aware and conversational recommender systems (KARS/ComplexRec) at RecSys 2021." ACM SIGIR Forum 55, no. 2 (2021): 1–9. http://dx.doi.org/10.1145/3527546.3527566.

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In this report, we offer a brief overview of the contributions and takeaways from the Joint KaRS & ComplexRec Workshop, co-located with the 15 th edition of the ACM RecSys in Amsterdam, The Netherlands. With this workshop, we aimed to merge the main objectives envisioned for the 3 rd Edition of the Workshop of Knowledge-aware and Conversational Recommender Systems and the 5 th Edition of the Workshop on Recommendation in Complex Environments. This joint workshop adopted a hybrid format aligned with the goal of this year's main conference congregating to continue to build community around r

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Bogers, Toine, and Marijn Koolen. "Report on RecSys 2015 Workshop on New Trends in Content-Based Recommender Systems." ACM SIGIR Forum 49, no. 2 (2016): 141–46. http://dx.doi.org/10.1145/2888422.2888445.

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15

Zangerle, Eva, Christine Bauer, and Alan Said. "Report on the 1st workshop on the perspectives on the evaluation of recommender systems (PERSPECTIVES 2021) at RecSys 2021." ACM SIGIR Forum 55, no. 2 (2021): 1–5. http://dx.doi.org/10.1145/3527546.3527565.

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Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2021 workshop at the 15th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current state of evaluation from different, and maybe even diverging or contradictory perspectives. Date : 25 and 30 September 2021. Website : https://perspectives-ws.github.io/2021/.

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Zangerle, Eva, Christine Bauer, and Alan Said. "Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022." ACM SIGIR Forum 56, no. 2 (2022): 1–4. http://dx.doi.org/10.1145/3582900.3582919.

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Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2022 workshop at the 16th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current state of evaluation from different, and maybe even diverging or contradictory perspectives. Date: 22 September, 2022. Website: https://perspectives-ws.github.io/2022/.

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17

Dacrema, Maurizio Ferrari, Pablo Castells, Justin Basilico, and Paolo Cremonesi. "Report on the Workshop on Learning and Evaluating Recommendations with Impressions (LERI) at RecSys 2023." ACM SIGIR Forum 57, no. 2 (2023): 1–8. http://dx.doi.org/10.1145/3642979.3643001.

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The Workshop on Learning and Evaluating Recommendations with Impressions (LERI) was held in conjunction with the 17th ACM Conference on Recommender Systems (RecSys 2023). The program included a keynote, a panel discussion and 7 paper presentations. The proceedings of the workshop are available online.1 The LERI workshop focused on all aspects related to the use of impressions for recommendation with the aim to bring the community together and share experience and perspectives. Recommender systems typically rely on past user interactions as the primary source of information for making predictio

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18

Pera, Maria Soledad, Jerry Alan Fails, Mirko Gelsomini, and Franca Garzotto. "Building Community: Report on KidRec Workshop on Children and Recommender Systems at RecSys 2017." ACM SIGIR Forum 52, no. 1 (2018): 153–61. http://dx.doi.org/10.1145/3274784.3274803.

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19

Azeroual, Otmane, and Tibor Koltay. "RecSys Pertaining to Research Information with Collaborative Filtering Methods: Characteristics and Challenges." Publications 10, no. 2 (2022): 17. http://dx.doi.org/10.3390/publications10020017.

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Recommendation (recommender) systems have played an increasingly important role in both research and industry in recent years. In the area of publication data, for example, there is a strong need to help people find the right research information through recommendations and scientific reports. The difference between search engines and recommendation systems is that search engines help us find something we already know, while recommendation systems are more likely to help us find new items. An essential function of recommendation systems is to support users in their decision making. Recommendat

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20

Chua, Tat-Seng. "Towards Generative Search and Recommendation: A keynote at RecSys 2023." ACM SIGIR Forum 57, no. 2 (2023): 1–14. http://dx.doi.org/10.1145/3642979.3642986.

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The emergence of large language models (LLM's), especially ChatGPT, has for the first time make AI known to almost everyone and affected every facet of our society. The LLMs have the potential to revolutionize the ways we seek and consume information. This has stemmed the recent trends in both academia and industry to develop LLM-based generative AI systems for various applications with enhanced capabilities. One such systems is the generative search and recommender system, which is capable of performing content retrieval, content repurposing, content creation and their integration to meet use

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21

Mansoury, Masoud. ""Understanding and mitigating multi-sided exposure bias in recommender systems" by Masoud Mansoury with Aparna S. Varde as coordinator." ACM SIGWEB Newsletter, Autumn (September 2022): 1–4. http://dx.doi.org/10.1145/3566100.3566103.

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Masoud Mansoury is a postdoctoral researcher at Amsterdam Machine Learning Lab at University of Amsterdam, Netherlands. He is also a member of Discovery Lab collaborating with Data Science team at Elsevier Company in the area of recommender systems. Masoud received his PhD in Computer and Information Science from Eindhoven University of Technology, Netherlands, in 2021. He has published his research works in top conferences such as FAccT, RecSys, and CIKM. His research interests include recommender systems, algorithmic bias, and contextual bandits. This research conducted by Masoud Mansoury in

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22

Ouyang, Zhongyu, Chunhui Zhang, Yaning Jia, and Soroush Vosoughi. "Scaled Supervision is an Implicit Lipschitz Regularizer." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 1419–35. https://doi.org/10.1609/icwsm.v19i1.35880.

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In modern social media, recommender systems (RecSys) rely on the click-through rate (CTR) as the standard metric to evaluate user engagement. CTR prediction is traditionally framed as a binary classification task to predict whether a user will interact with a given item. However, this approach overlooks the complexity of real-world social modeling, where user, item, and their interactive features change dynamically in fast-paced online environments. This dynamic nature often leads to model instability, reflected in overfitting short-term fluctuations rather than higher-level interactive patter

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23

Boyer, Lea Evangeline, Mathieu Boudier-Revéret, and Min Cheol Chang. "Protocol for lower back pain management: Insights from the French healthcare system." World Journal of Clinical Cases 12, no. 11 (2024): 1875–80. http://dx.doi.org/10.12998/wjcc.v12.i11.1875.

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In this editorial we comment on the article published in a recent issue of the World Journal of Clinical Cases . This article described a novel ultrasound-guided lateral recess block approach in treating a patient with lateral recess stenosis. The impact of spinal pain-related disability extends significantly, causing substantial human suffering and medical costs. Each county has its preferred treatment strategies for spinal pain. Here, we explore the lower back pain (LBP) treatment algorithm recommended in France. The treatment algorithm for LBP recommended by the French National Authority fo

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24

Rodziewicz, Joanna, Artur Mielcarek, Wojciech Janczukowicz, Kamil Bryszewski, Agata Jabłońska-Trypuć, and Urszula Wydro. "Technological Parameters of Rotating Electrochemical and Electrobiological Disk Contactors Depending on the Effluent Quality Requirements." Applied Sciences 12, no. 11 (2022): 5503. http://dx.doi.org/10.3390/app12115503.

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Soilless tomato cultivation wastewater, with typically low COD, high concentrations of phosphorus, and oxidized forms of nitrogen, may be effectively treated in a rotating electrochemical disk contactor (RECDC) and in a bioelectrochemical reactor (BER), such as a rotating electrobiological disk contactor (REBDC). The aim of this study was to determine the technological parameters of both reactors, i.e., electric current density (J) and hydraulic retention time (HRT), depending on the effluent quality requirements. The study was conducted with four one-stage RECDCs and with four one-stage REBDC

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25

del Valle, Juan Ignacio, and Francisco Lara. "AI-powered recommender systems and the preservation of personal autonomy." AI & SOCIETY, July 21, 2023. http://dx.doi.org/10.1007/s00146-023-01720-2.

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AbstractRecommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did not come with a thorough consideration of their ethical implications and, despite being a

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Kalisvaart, Raoul, Masoud Mansoury, Alan Hanjalic, and Elvin Isufi. "Towards Carbon Footprint-Aware Recommender Systems for Greener Item Recommendation." ACM Transactions on Recommender Systems, May 9, 2025. https://doi.org/10.1145/3735144.

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The commodity and widespread use of online shopping are having an unprecedented impact on climate, with emission figures from key actors that are easily comparable to those of a large-scale metropolis. Despite online shopping being fueled by recommender systems (RecSys) algorithms, the role and potential of the latter in promoting more sustainable choices is little studied. One of the main reasons for this could be attributed to the lack of a dataset containing carbon footprint emissions for the items. While building such a dataset is a rather challenging task, its presence is pivotal for open

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Ceh-Varela, Edgar, Huiping Cao, and Hady W. Lauw. "Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups." ACM Transactions on Intelligent Systems and Technology, June 3, 2022. http://dx.doi.org/10.1145/3542804.

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Recommender Systems ( RecSys ) provide suggestions in many decision-making processes. Given that groups of people can perform many real-world activities (e.g., a group of people attending a conference looking for a place to dine), the need for recommendations for groups has increased. A wide range of Group Recommender Systems ( GRecSys ) has been developed to aggregate individual preferences to group preferences. We analyze 175 studies related to GRecSys . Previous works evaluate their systems using different types of groups (sizes and cohesiveness), and most of such works focus on testing the

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28

Caceres, Ileana, and Souvick Ghosh. "The sound of music: from increased personalization to therapeutic values." Information Research: an international electronic journal 27 (2022). http://dx.doi.org/10.47989/irisic2201.

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Music providers like Spotify leverage music recommendation systems to connect users with relevant music. Based on content-based and collaborative-filtering statistical methods, these machine learning algorithms quantify user-song probabilities and present the highest-ranked songs. However, most music providers do not fully address their users’ music seeking and retrieval needs. Likewise, the fields of Recommender Systems (RecSys), Music Recommendation Systems (MRS) and Music Information Retrieval (MIR) remain disconnected from real-world use cases of music seeking. In this conceptual paper, we

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29

Sharma, Kartik, Yeon-Chang Lee, Sivagami Nambi, et al. "A Survey of Graph Neural Networks for Social Recommender Systems." ACM Computing Surveys, April 29, 2024. http://dx.doi.org/10.1145/3661821.

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Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users’ tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of graph neural networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the l

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30

Nguyen, Thanh Toan, Nguyen Quoc Viet Hung, Thanh Tam Nguyen, et al. "Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures." ACM Computing Surveys, July 25, 2024. http://dx.doi.org/10.1145/3677328.

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Recommender systems have become an integral part of online services due to their ability to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks particularly those that involve learning schemes. A poisoning attack is where an adversary injects carefully crafted data into the process of training a model, with the goal of manipulating the system’s final recommendations. Based on recent advancements in artificial intelligence (AI), such attacks have gained importance recently. At present, we do no

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31

Tran, Hung Vinh, Tong Chen, Nguyen Quoc Viet Hung, Zi Huang, Lizhen Cui, and Hongzhi Yin. "A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems." ACM Transactions on Information Systems, January 17, 2025. https://doi.org/10.1145/3712589.

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Since the creation of the Web, recommender systems (RSs) have been an indispensable personalization mechanism in information filtering. Most state-of-the-art RSs primarily depend on categorical features such as user and item IDs, and use embedding vectors to encode their information for accurate recommendations, resulting in an excessively large embedding table owing to the immense feature corpus. To prevent the heavily parameterized embedding table from harming RSs’ scalability, both academia and industry have seen increasing efforts compressing RS embeddings, and this trend is further amplif

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32

"An Efficient Recommender System Technique in Social Networks Based on Association Rule Based Mining." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (2019): 3437–47. http://dx.doi.org/10.35940/ijitee.i8422.078919.

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A recommender system is an information filtering system that has become a buzzword in various areas of marketing and research such as movies, music, books, products and research articles. The main role of recommender systems is to guide users on a personal level to provide an optimum set of suggestions based on the users’ taste, explicit rating of items, his/her demographic and other related valuable information. In the past decade, several approaches have been discussed for recommendation of items to online users keeping in mind the accuracy of prediction, the cold-start problem and the probl

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33

Bahati, Monica Karungi, Ronald. "STUDENT EVALUATION AS AN IMPETUS FOR QUALITY TEACHING AND LEARNING IN HIGHER EDUCATION INSTITUTIONS: THE EXPERIENCE OF BISHOP STUART UNIVERSITY." African Multidisciplinary Journal of Research, February 23, 2022. https://doi.org/10.71064/spu.amjr.1.1.72.

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In the context of sustained growth and diversification of Higher Education Systems, civil society is increasingly concerned about the quality of programmers offered to students. As a result, there is an increase in public assessments and international comparisons of Higher Education Institutions, not only within the higher education sector but in the general media (OECD, 2008). However, evaluation methods tend to overemphasize research and the use of research performance as a yardstick of an institution’s value. Although this is very paramount in academia, it has got insignificant contribution

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