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Journal articles on the topic 'Large Scale Recommendation'

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1

Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Large Scale Multilingual Sticker Recommendation In Messaging Apps." AI Magazine 42, no. 4 (2022): 16–28. http://dx.doi.org/10.1609/aimag.v42i4.15098.

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Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except
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2

Zhou, Wang, Yongluan Zhou, Jianping Li, and Muhammad Hammad Memon. "LsRec: Large-scale social recommendation with online update." Expert Systems with Applications 162 (December 2020): 113739. http://dx.doi.org/10.1016/j.eswa.2020.113739.

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Sakhi, Otmane, David Rohde, and Alexandre Gilotte. "Fast Offline Policy Optimization for Large Scale Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9686–94. http://dx.doi.org/10.1609/aaai.v37i8.26158.

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Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these systems can be achieved by a relaxation of the discrete problem resulting in policy learning or REINFORCE style learning algorithms. Unfortunately, this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world example
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Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Large Scale Multilingual Sticker Recommendation In Messaging Apps." AI Magazine 42, no. 4 (2022): 16–28. http://dx.doi.org/10.1609/aaai.12023.

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Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except
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5

Chen, Haokun, Xinyi Dai, Han Cai, et al. "Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3312–20. http://dx.doi.org/10.1609/aaai.v33i01.33013312.

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Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the cont
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Liu, Yang, Cheng Lyu, Zhiyuan Liu, and Jinde Cao. "Exploring a large-scale multi-modal transportation recommendation system." Transportation Research Part C: Emerging Technologies 126 (May 2021): 103070. http://dx.doi.org/10.1016/j.trc.2021.103070.

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7

E, HaiHong, JianFeng WANG, MeiNa SONG, Qiang BI, and 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.

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HASHIMOTO, 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.

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9

Khan, Muhammad Usman Shahid, Osman Khalid, Ying Huang, et al. "MacroServ: A Route Recommendation Service for Large-Scale Evacuations." IEEE Transactions on Services Computing 10, no. 4 (2017): 589–602. http://dx.doi.org/10.1109/tsc.2015.2497241.

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10

Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Scalable Recommendation Using Large Scale Graph Partitioning With Pregel and Giraph." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 4 (2020): 42–61. http://dx.doi.org/10.4018/ijcini.2020100103.

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Social Big Data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in social Big Data. If any user intends to select products such as movies, books, etc., from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social
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11

Xu, Ruzhi, Shuaiqiang Wang, Xuwei Zheng, and Yinong Chen. "Distributed collaborative filtering with singular ratings for large scale recommendation." Journal of Systems and Software 95 (September 2014): 231–41. http://dx.doi.org/10.1016/j.jss.2014.04.045.

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12

Hu, Long, Kai Lin, Mohammad Mehedi Hassan, Atif Alamri, and Abdulhameed Alelaiwi. "CFSF: On Cloud-Based Recommendation for Large-Scale E-commerce." Mobile Networks and Applications 20, no. 3 (2015): 380–90. http://dx.doi.org/10.1007/s11036-014-0560-5.

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13

Schall, Daniel. "Who to follow recommendation in large-scale online development communities." Information and Software Technology 56, no. 12 (2014): 1543–55. http://dx.doi.org/10.1016/j.infsof.2013.12.003.

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14

Bharath, Bhushan Yannam. "EFFECTIVE RECOMMENDATION CHAINS FOR LARGE SCALE DISTRIBUTED DECENTRALIZED P2P SYSTEMS." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 09 (2013): 300–306. https://doi.org/10.5281/zenodo.14613399.

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The security model used for centralized systems is not suitable for P2P networks as it is centralized in nature. The security challenges in the P2P networks are secure reputation data management, availability of reputation data, Sybil attacks and identity management of peers. In this paper we present a cryptographic protocol for ensuring secure and timely availability of the reputation data of a peer extremely at low cost. We also investigate Reputation Systems for P2P networks more ambitious approach to protect the P2P network without using any central component, and thereby harnessing the fu
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15

Sang Hyun Choi, Young-Seon Jeong, and Myong K. Jeong. "A Hybrid Recommendation Method with Reduced Data for Large-Scale Application." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40, no. 5 (2010): 557–66. http://dx.doi.org/10.1109/tsmcc.2010.2046036.

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16

Trinh, Trang, Van-Ho Nguyen, Nghia Nguyen, and Duy-Nghia Nguyen. "Product collaborative filtering based recommendation systems for large-scale E-commerce." International Journal of Information Management Data Insights 5, no. 1 (2025): 100322. https://doi.org/10.1016/j.jjimei.2025.100322.

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17

Ma, Yue, Guoqing Chen, and Qiang Wei. "Finding users preferences from large-scale online reviews for personalized recommendation." Electronic Commerce Research 17, no. 1 (2016): 3–29. http://dx.doi.org/10.1007/s10660-016-9240-9.

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18

Du, Changyu, Zihan Deng, Stavros Nousias, and André Borrmann. "Predictive modeling: BIM command recommendation based on large-scale usage logs." Advanced Engineering Informatics 68 (November 2025): 103574. https://doi.org/10.1016/j.aei.2025.103574.

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19

Shin, Kyuyong, Hanock Kwak, Su Young Kim, et al. "Scaling Law for Recommendation Models: Towards General-Purpose User Representations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4596–604. http://dx.doi.org/10.1609/aaai.v37i4.25582.

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Recent advancement of large-scale pretrained models such as BERT, GPT-3, CLIP, and Gopher, has shown astonishing achievements across various task domains. Unlike vision recognition and language models, studies on general-purpose user representation at scale still remain underexplored. Here we explore the possibility of general-purpose user representation learning by training a universal user encoder at large scales. We demonstrate that the scaling law is present in user representation learning areas, where the training error scales as a power-law with the amount of computation. Our Contrastive
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20

Shen, Lijuan, and Liping Jiang. "Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization." PeerJ Computer Science 10 (February 29, 2024): e1858. http://dx.doi.org/10.7717/peerj-cs.1858.

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Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children’s book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users’ book ratings serving as the weights of the edge
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21

Chaitanya., G., Y. Hemanth., K. Koushik., P. Haneef., and Pranav.S. "Movie Recomondation System using Machine Learning and Spark." International Journal of Innovative Science and Research Technology (IJISRT) 8, no. 6 (2024): 6. https://doi.org/10.5281/zenodo.10686713.

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In this abstract, we present a cutting- edge movie recommendation system that combines the power of machine learning algorithms with the scalability and speed of the Spark framework. Our system is designed to deliver highly accurate and personalized movie recommendations to users by analyzing their viewing history, preferences, and demographic information. By leveraging Spark's distributed computing capabilities, we efficiently process large-scale movie datasets and train complex recommendation models in parallel. The results of our experiments demonstrate the system's superior recommendation
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22

Sun, Juan. "Personalized Music Recommendation Algorithm Based on Spark Platform." Computational Intelligence and Neuroscience 2022 (February 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/7157075.

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Aiming at the shortcomings of traditional recommendation algorithms in dealing with large-scale music data, such as low accuracy and poor real-time performance, a personalized recommendation algorithm based on the Spark platform is proposed. The algorithm is based on the Spark platform. The K-means clustering model between users and music is constructed using an AFSA (artificial fish swarm algorithm) to optimize the initial centroids of K-means to improve the clustering effect. Based on the scoring relationship between users and users and users and music attributes, the collaborative filtering
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23

Lulla, Karan. "Designing Fault-Tolerant Test Infrastructure for Large-Scale GPU Manufacturing." International journal of signal processing, embedded systems and VLSI design 5, no. 1 (2025): 35–61. https://doi.org/10.55640/ijvsli-05-01-04.

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In a modern-day digital economy, computational requirements for high-stakes industries such as finance, real estate, retail, and cloud computing must be met by Graphics Processing Units (GPUs). Reliability and performance of such GPUs are integral, as small failures can cause large-scale business disruptions and financial losses. This paper examines the architectural and methodological models for designing a fault-tolerant test infrastructure in the large-scale production of GPUs. It highlights the requirement of redundancy, modularity, real-time monitoring, and automated error check prototypi
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24

Shi, Jiatu, Fu Shang, Shuwen Zhou, Xu Zhang, and Gang Ping. "Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy." Journal of Industrial Engineering and Applied Science 2, no. 4 (2024): 90–103. https://doi.org/10.5281/zenodo.13117899.

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This paper presents a novel quantum-enhanced recommendation system for large-scale e-commerce platforms, addressing the challenges of computational complexity and scalability in traditional approaches. We introduce a hybrid quantum-classical architecture that leverages quantum principal component analysis (qPCR) for efficient feature extraction and quantum similarity computation for improved recommendation accuracy. Our system demonstrates significant performance improvements over classical methods, achieving an 87.3% reduction in execution time and a 15.8% increase in precision@10 across dive
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25

Zhu, Jianke, Hao Ma, Chun Chen, and Jiajun Bu. "Social Recommendation Using Low-Rank Semidefinite Program." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 158–63. http://dx.doi.org/10.1609/aaai.v25i1.7837.

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The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous approaches, that are based on the conventional gradient descent optimization, we formulate the presented graph Laplacian regularized social recommendation problem into a low-rank semidefinite program, whic
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26

Noei, Ehsan, Tsahi Hayat, Jessica Perrie, et al. "A qualitative study of large-scale recommendation algorithms for biomedical knowledge bases." International Journal on Digital Libraries 22, no. 2 (2021): 197–215. http://dx.doi.org/10.1007/s00799-021-00300-3.

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27

Kanavos, Andreas, Stavros Iakovou, Spyros Sioutas, and Vassilis Tampakas. "Large Scale Product Recommendation of Supermarket Ware Based on Customer Behaviour Analysis." Big Data and Cognitive Computing 2, no. 2 (2018): 11. http://dx.doi.org/10.3390/bdcc2020011.

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28

Jianping Fan, D. A. Keim, Yuli Gao, Hangzai Luo, and Zongmin Li. "JustClick: Personalized Image Recommendation via Exploratory Search From Large-Scale Flickr Images." IEEE Transactions on Circuits and Systems for Video Technology 19, no. 2 (2009): 273–88. http://dx.doi.org/10.1109/tcsvt.2008.2009258.

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29

Kashef, Rasha. "Enhancing the Role of Large-Scale Recommendation Systems in the IoT Context." IEEE Access 8 (2020): 178248–57. http://dx.doi.org/10.1109/access.2020.3026310.

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30

Chou, Szu-Yu, Jyh-Shing Roger Jang, and Yi-Hsuan Yang. "Fast Tensor Factorization for Large-Scale Context-Aware Recommendation from Implicit Feedback." IEEE Transactions on Big Data 6, no. 1 (2020): 201–8. http://dx.doi.org/10.1109/tbdata.2018.2889121.

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31

Coward, L. Andrew. "The recommendation architecture: lessons from large-scale electronic systems applied to cognition." Cognitive Systems Research 2, no. 2 (2001): 111–56. http://dx.doi.org/10.1016/s1389-0417(01)00024-9.

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32

Li, Chaoyi, and Yangsen Zhang. "A personalized recommendation algorithm based on large-scale real micro-blog data." Neural Computing and Applications 32, no. 15 (2020): 11245–52. http://dx.doi.org/10.1007/s00521-020-05042-y.

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33

Zhang, Hailin, Zirui Liu, Boxuan Chen, et al. "CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–28. http://dx.doi.org/10.1145/3639306.

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Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key design requirements: memory efficiency, low latency, and adaptability to dynamic data distribution. This paper presents CAFE, a Compact, Adaptive, and Fast Embedding compression framework that addresses the above requirements. The design philosophy of CAFE is to dynamically allocate more memory resources to important features (called hot features), and alloca
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Park, Min-Soo, Dong-Yeong Lee, Jae-Soo Yoo, and Do-Jin Choi. "Context-learning based Academic Reference Recommendation System using Large-scale Academic Datasets." JOURNAL OF THE KOREA CONTENTS ASSOCIATION 25, no. 3 (2025): 50–61. https://doi.org/10.5392/jkca.2025.25.03.050.

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Yang, Haini. "Application Analysis of English Personalized Learning Based on Large-scale Open Network Courses." Scalable Computing: Practice and Experience 25, no. 1 (2024): 355–68. http://dx.doi.org/10.12694/scpe.v25i1.2300.

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In the context of Big data, large-scale open online courses increase learning paths for learners, but in the face of countless high-quality curriculum resources, it is easy for derivative learners to face the dilemma of rich curriculum resources but difficult to choose resources, which leads to information maze for learners. How to help learners quickly and accurately find their own learning resources in the explosive growth of MOOC resources is an urgent problem in the field of education Big data. However, the traditional Collaborative filtering recommendation technology does not perform well
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36

Lesner, Christopher, Alexander Ran, Marko Rukonic, and Wei Wang. "Large Scale Personalized Categorization of Financial Transactions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9365–72. http://dx.doi.org/10.1609/aaai.v33i01.33019365.

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A major part of financial accounting involves tracking and organizing business transactions over and over each month and hence automation of this task is of significant value to the users of accounting software. In this paper we present a large-scale recommendation system that successfully recommends company specific categories for several million small businesses in US, UK, Australia, Canada, India and France and handles billions of financial transactions each year. Our system uses machine learning to combine fragments of information from millions of users in a manner that allows us to accura
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Li, Chen, Annisa Annisa, Asif Zaman, Mahboob Qaosar, Saleh Ahmed, and Yasuhiko Morimoto. "MapReduce Algorithm for Location Recommendation by Using Area Skyline Query." Algorithms 11, no. 12 (2018): 191. http://dx.doi.org/10.3390/a11120191.

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Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed a new query method, called “area skyline query”, to select areas in a map. However, it is not efficient for large-scale data. In this paper, we propose a parallel algorithm for processing the area skyline using MapReduce. Intensive experiments on both synthetic and real dat
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Lesner, Christopher, Alexander Ran, Marko Rukonic, and Wei Wang. "Large Scale Personalized Categorization of Financial Transactions." AI Magazine 41, no. 3 (2020): 63–77. http://dx.doi.org/10.1609/aimag.v41i3.5319.

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A major part of financial accounting involves organizing business transactions using a customizable filing system that accountants call a “chart of accounts.” This task must be carried out for every financial transaction, and hence automation is of significant value to the users of accounting software. In this article we present a large-scale recommendation system used by millions of small businesses in the USA, UK, Australia, Canada, India, and France to organize billions of financial transactions each year. The system uses machine learning to combine fragments of information from millions of
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39

Luo, Ning, and Linlin Zhang. "Smart ULT Management for Ultra-Large-Scale Software." International Journal of Software Engineering & Applications 13, no. 4 (2022): 15–22. http://dx.doi.org/10.5121/ijsea.2022.13402.

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The importance of development ULT (unit level test) is of no doubt today. But deployment of ULT in ultralarge-scale software till sufficient coverage requires big development effort while it could be hard for developers to precisely identify the error prone logics deserving the best test coverage. In this paper, we propose one novel Smart ULT Management system or automatic ULT deployment on ultra-large-scale software which can provide the test coverage recommendation, and automatically generate >80% ULT code. It helps us greatly shrink the average ULT code development effort from ~24 Man ho
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Ning, Luo, and Zhang Linlin. "Smart ULT Management for Ultra-Large-Scale Software." International Journal of Software Engineering & Applications (IJSEA) 13, no. 4 (2022): 15–22. https://doi.org/10.5281/zenodo.7119008.

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The importance of development ULT (unit level test) is of no doubt today. But deployment of ULT in ultralarge-scale software till sufficient coverage requires big development effort while it could be hard for developers to precisely identify the error prone logics deserving the best test coverage. In this paper, we propose one novel Smart ULT Management system or automatic ULT deployment on ultra-large-scale software which can provide the test coverage recommendation, and automatically generate >80% ULT code. It helps us greatly shrink the average ULT code development effort from ~24 Man ho
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Ning, Luo. "Smart ULT Management for Ultra-Large-Scale Software." International Journal of Software Engineering & Applications (IJSEA) 13, no. 4 (2023): 15–22. https://doi.org/10.5281/zenodo.8296947.

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The importance of development ULT (unit level test) is of no doubt today. But deployment of ULT in ultralarge-scale software till sufficient coverage requires big development effort while it could be hard for developers to precisely identify the error prone logics deserving the best test coverage. In this paper, we propose one novel Smart ULT Management system or automatic ULT deployment on ultra-large-scale software which can provide the test coverage recommendation, and automatically generate >80% ULT code. It helps us greatly shrink the average ULT code development effort from ~24 Man ho
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Yochum, Phatpicha, Liang Chang, Tianlong Gu, and Manli Zhu. "Learning Sentiment over Network Embedding for Recommendation System." International Journal of Machine Learning and Computing 11, no. 1 (2021): 12–20. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1008.

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With the rapid development of Internet, various unstructured information, such as user-generated content, textual reviews, and implicit or explicit feedbacks have grown continuously. Though structured knowledge bases (KBs) which consist of a large number of triples exhibit great advantages in recommendation field recently. In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding method with the sentiment of user reviews. Specifically, we use t
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43

Zhou, Xiaokang, Wei Liang, Suzhen Huang, and Miao Fu. "Social Recommendation With Large-Scale Group Decision-Making for Cyber-Enabled Online Service." IEEE Transactions on Computational Social Systems 6, no. 5 (2019): 1073–82. http://dx.doi.org/10.1109/tcss.2019.2932288.

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44

Zhang, Weina, Xingming Zhang, Haoxiang Wang, and Dongpei Chen. "A deep variational matrix factorization method for recommendation on large scale sparse dataset." Neurocomputing 334 (March 2019): 206–18. http://dx.doi.org/10.1016/j.neucom.2019.01.028.

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45

Nie, Na. "Research on Personalized Recommendation Algorithm of Internet Platform Goods Based on Knowledge Graph." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 415–22. http://dx.doi.org/10.54097/hset.v56i.10704.

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Personalized recommendation method is an effective means to filter out the information users need from a large amount of information, which is rich in practical value. Personalized recommendation methods are maturing, and many e-commerce platforms have been using different forms of recommendation methods with great success. In the recommendation systems of large-scale e-commerce platforms, traditional recommendation algorithms represented by collaborative filtering are modeled only based on users' rating data, and sparse user-project interaction data and cold start are two inevitable problems.
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Bhaskaran, S., Raja Marappan, and B. Santhi. "Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets." Mathematics 8, no. 7 (2020): 1106. http://dx.doi.org/10.3390/math8071106.

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Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset,
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47

Qiao, Yu, Kaixian Xu, and Alan Wilson. "Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis." Artificial Intelligence Advances 7, no. 1 (2025): 10–21. https://doi.org/10.30564/aia.v7i1.9761.

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Real-time personalized ad recommendation systems are crucial for enhancing user engagement and satisfaction. To address the challenge of delivering highly relevant ads in a dynamic, large-scale environment, this paper proposes a novel approach that integrates real-time user behavior analysis with advanced time series modeling and stream processing techniques. Specifically, the system leverages Long Short-Term Memory (LSTM) networks to capture both short-term and long-term user preferences, ensuring accurate and personalized ad recommendations. By utilizing stream processing frameworks like Apa
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48

Shi, Chenxi, Penghao Liang, Yichao Wu, Tong Zhan, and Zhengyu Jin. "Maximizing user experience with LLMOps-driven personalized recommendation systems." Applied and Computational Engineering 64, no. 1 (2024): 102–8. http://dx.doi.org/10.54254/2755-2721/64/20241353.

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The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities and challenges for enterprises, requiring specialized teams to navigate the complexity of engineering technology while prioritizing data security and model interpretability. By leveraging LLMOps, enterprises can enhance the efficiency and reliability of large-scale machine learning models, driving personalized recommendations aligned with user preferences. Despite ethical considerations, LLMOps is poised for widespre
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Kalloubi, Fahd, El Habib Nfaoui, and Omar El Beqqali. "Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms." International Journal on Semantic Web and Information Systems 13, no. 1 (2017): 63–81. http://dx.doi.org/10.4018/ijswis.2017010105.

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Twitter is one of the most popular microblog service providers, in this microblogging platform users use hashtags to categorize their tweets and to join communities around particular topics. However, the percentage of messages incorporating hashtags is small and the hashtags usage is very heterogeneous as users may spend a lot of time searching the appropriate hashtags for their messages. In this paper, the authors present an approach for hashtag recommendations in microblogging platforms by leveraging semantic features. Moreover, they conduct a detailed study on how the semantic-based model i
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Wan, Xiangpeng, Hakim Ghazzai, and Yehia Massoud. "A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services." Electronics 9, no. 4 (2020): 648. http://dx.doi.org/10.3390/electronics9040648.

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Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers’ quality of experience and drivers’ benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key p
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