Journal articles: 'Large Scale Recommendation'

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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 (January 12, 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 for validation, our system does not require human labeled data, leading to a fully auto-matic tuning pipeline. We propose a hybrid message prediction model, which can easily run on low-end phones. We discuss message cluster to sticker mapping, addressing the multilingual needs of our users, automated tuning of the system and also propose a novel application of community detection algorithm. As of November 2020, our system contains 100k+ stickers, has been deployed for 15+ months, and is being used by millions of users.

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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 (June 26, 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 examples such as large catalogue recommender systems, severely limiting the usefulness of this method in practice. In this paper, we derive an approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show that our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.

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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 (January 18, 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 for validation, our system does not require human labeled data, leading to a fully auto-matic tuning pipeline. We propose a hybrid message prediction model, which can easily run on low-end phones. We discuss message cluster to sticker mapping, addressing the multilingual needs of our users, automated tuning of the system and also propose a novel application of community detection algorithm. As of November 2020, our system contains 100k+ stickers, has been deployed for 15+ months, and is being used by millions of users.

5

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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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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Chen, Haokun, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, and Yong Yu. "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 continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.

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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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Khan, Muhammad Usman Shahid, Osman Khalid, Ying Huang, Rajiv Ranjan, Fan Zhang, Junwei Cao, Bharadwaj Veeravalli, Samee U. Khan, Keqin Li, and Albert Y. Zomaya. "MacroServ: A Route Recommendation Service for Large-Scale Evacuations." IEEE Transactions on Services Computing 10, no. 4 (July 1, 2017): 589–602. http://dx.doi.org/10.1109/tsc.2015.2497241.

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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 (October 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 regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity, but not efficient for scalability. The main focus of this article is to improve scalability in terms of locality and throughput and provides better recommendations to users with large-scale data in less response time. In this article, the social big graph is partitioned and distributed on different nodes based on Pregel and Giraph. In the proposed approach ScaleRec, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using proposed approach. In ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets such as Epinions and LiveJournal proves that better locality and recommendation accuracy is achieved by using ScaleRec.

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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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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 (January 30, 2015): 380–90. http://dx.doi.org/10.1007/s11036-014-0560-5.

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

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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 (September 2010): 557–66. http://dx.doi.org/10.1109/tsmcc.2010.2046036.

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Ma, Yue, Guoqing Chen, and Qiang Wei. "Finding users preferences from large-scale online reviews for personalized recommendation." Electronic Commerce Research 17, no. 1 (October 8, 2016): 3–29. http://dx.doi.org/10.1007/s10660-016-9240-9.

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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 edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children’s book recommendation systems.

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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 algorithm is applied to calculate the correlation between users to achieve accurate recommendations. Finally, the performance of the designed recommendation model is validated by deploying the recommendation model on the Spark platform using the Yahoo Music dataset and online music platform dataset. The experimental results show that the use of improved AFSA can complete the optimization of K-means clustering centroids with good clustering results; combined with the distributed fast computing capability of Spark platform with multiple nodes, the recommendation accuracy has better performance than traditional recommendation algorithms; especially when dealing with large-scale music data, the recommendation accuracy and real-time performance are higher, which meet the current demand of personalized music recommendation.

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Shin, Kyuyong, Hanock Kwak, Su Young Kim, Max Nihlén Ramström, Jisu Jeong, Jung-Woo Ha, and Kyung-Min Kim. "Scaling Law for Recommendation Models: Towards General-Purpose User Representations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 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 Learning User Encoder (CLUE), optimizes task-agnostic objectives, and the resulting user embeddings stretch our expectation of what is possible to do in various downstream tasks. CLUE also shows great transferability to other domains and companies, as performances on an online experiment shows significant improvements in Click-Through-Rate (CTR). Furthermore, we also investigate how the model performance is influenced by the scale factors, such as training data size, model capacity, sequence length, and batch size. Finally, we discuss the broader impacts of CLUE in general.

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Noei, Ehsan, Tsahi Hayat, Jessica Perrie, Recep Çolak, Yanqi Hao, Shankar Vembu, Kelly Lyons, and Sam Molyneux. "A qualitative study of large-scale recommendation algorithms for biomedical knowledge bases." International Journal on Digital Libraries 22, no. 2 (April 19, 2021): 197–215. http://dx.doi.org/10.1007/s00799-021-00300-3.

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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 (May 9, 2018): 11. http://dx.doi.org/10.3390/bdcc2020011.

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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 (February 2009): 273–88. http://dx.doi.org/10.1109/tcsvt.2008.2009258.

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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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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 (March 1, 2020): 201–8. http://dx.doi.org/10.1109/tbdata.2018.2889121.

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

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

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Zhang, Hailin, Zirui Liu, Boxuan Chen, Yikai Zhao, Tong Zhao, Tong Yang, and Bin Cui. "CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models." Proceedings of the ACM on Management of Data 2, no. 1 (March 12, 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 allocate less memory to unimportant ones. In CAFE, we propose a fast and lightweight sketch data structure, named HotSketch, to capture feature importance and report hot features in real time. For each reported hot feature, we assign it a unique embedding. For the non-hot features, we allow multiple features to share one embedding by using hash embedding technique. Guided by our design philosophy, we further propose a multi-level hash embedding framework to optimize the embedding tables of non-hot features. We theoretically analyze the accuracy of HotSketch, and analyze the model convergence against deviation. Extensive experiments show that CAFE significantly outperforms existing embedding compression methods, yielding 3.92% and 3.68% superior testing AUC on Criteo Kaggle dataset and CriteoTB dataset at a compression ratio of 10000x. The source codes of CAFE are available at GitHub.

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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 accurately recommend user-specific Chart of Accounts categories. Accounts are handled even if named using abbreviations or in a foreign language. Transactions are handled even if a given user has never categorized a transaction like that before. The development of such a system and testing it at scale over billions of transactions is a first in the financial industry.

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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 (January 4, 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 when dealing with sparse data and cold start. The recommendation content is repeated and can not effectively deal with high-dimensional and nonlinear data of online learning users, resulting in low efficiency of resource recommendation. Therefore, the study adopts a deep belief network (DBN) to construct a personalized resource recommendation model. The model combines the learner behavior characteristics with the curriculum resource content attribute characteristics to form the learner feature vector. The parameters of the model are adjusted according to the characteristics of learners. Through experiments, the proposed model has shown good performance. The experiment explored the effects of training set size, learner characteristics, and GPU on model performance. The experimental results show that when the training set proportion is 100%, the RMSE, Accuracy, Recall, and F1 values of the model are 0.76, 0.946, 0.957, and 0.951, respectively. When the model is trained using a training set containing learner features, the RMSE, Accuracy, Recall, and F1 values of the model are 0.75, 0.962, 0.908, and 0.958, respectively. After using GPU to accelerate the model, the running time of the model decreased from 360 minutes to 90 minutes. The results indicate that the model cannot effectively mine data information when the degree of correlation between sample information is low. The richer the relationships between samples, the better the performance of the model. Simultaneously learning hunger feature vectors and learner behavior feature vectors for training can significantly improve the recommendation accuracy of the model. The main contribution of this study is to propose a recommendation method based on DBN classification to replace traditional similarity calculation methods, using DBN's efficient feature abstraction and feature extraction capabilities to fully explore learners' interest and preference for course resources. In addition, in view of the common problems of cold start and data sparsity in traditional Collaborative filtering recommendation methods, the research deeply mines the characteristics of learners' Demographics and curriculum resources' content attributes, and constructs a learner interest model based on DBN combined with learners' behavior characteristics, which effectively solves the problems of cold start and data sparsity, as well as the inaccurate expression of learners' interest preferences for curriculum resources.

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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 (August 4, 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, which is able to be efficiently solved by the quasi-Newton algorithm. We have conducted the empirical evaluation on a large scale dataset of high sparsity, the promising experimental results show that our method is very effective and efficient for the social recommendation task.

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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 (September 14, 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 users in a manner that allows us to accurately recommend chart-of-accounts categories even when users have created their own or named them using abbreviations or in foreign languages. Transactions are handled even if a given user has never categorized a transaction like that before. The development of such a system and testing it at scale over billions of transactions is a first in the financial industry.

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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 (November 25, 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 data confirm that our proposed algorithm is sufficiently efficient for large-scale data.

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Luo, Ning, and Linlin Zhang. "Smart ULT Management for Ultra-Large-Scale Software." International Journal of Software Engineering & Applications 13, no. 4 (July 31, 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 hours to ~3 Man hours per 1000 Lines of driver under test. We hope the experience shared can help more practitioners to apply the similar methodology.

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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 (January 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 the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and attractions and apply the user weight scoring which is the combination of user review ratings and textual reviews to get similar attractions among users. Experimental results on real-world dataset verified the superior recommendation performance on precision, recall, and F-measure of our approach compared with state-of-the-art baselines.

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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 (October 2019): 1073–82. http://dx.doi.org/10.1109/tcss.2019.2932288.

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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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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. The introduction of knowledge graphs in recommendation systems can effectively solve these problems because of their rich knowledge content and powerful relationship processing capability. In this paper, we study the personalized recommendation algorithm based on knowledge graph as auxiliary information, and use the temporal information of user-item interaction in the graph to model users' interests over time at a finer granularity, taking into account the problem of high training time cost of the model due to frequent updates of the knowledge graph when recommending to users dynamically. The article proposes the Interactive Knowledge-Aware Attention Network Algorithmic Model for Recommendations (IKANAM) and conducts comparison experiments on public datasets. The results show that the IKANAM recommendation algorithm can effectively improve the effectiveness of personalized recommendation of products on Internet platforms.

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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 (January 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 influences the final recommended hashtags using different ranking strategies. Also, users are interested by fresh and specific hashtags due to the rapid growth of microblogs, thus, the authors propose a time popularity ranking strategy. Furthermore, they study the combination of these ranking strategies. The experiment results conducted on a large dataset; show that their approach improves respectively lexical and semantic based recommendation by more than 11% and 7% on recommending 5 hashtags.

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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 (July 6, 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, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| < 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes <10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.

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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 (May 15, 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 widespread adoption, promising more efficient and secure machine learning services that elevate user experience and shape the future of personalized recommendation systems.

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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 (April 15, 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 performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.

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Zhang, Daqiang, Ching-Hsien Hsu, Min Chen, Quan Chen, Naixue Xiong, and Jaime Lloret. "Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems." IEEE Transactions on Emerging Topics in Computing 2, no. 2 (June 2014): 239–50. http://dx.doi.org/10.1109/tetc.2013.2283233.

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Corbellini, Alejandro, Cristian Mateos, Daniela Godoy, Alejandro Zunino, and Silvia Schiaffino. "An architecture and platform for developing distributed recommendation algorithms on large-scale social networks." Journal of Information Science 41, no. 5 (June 8, 2015): 686–704. http://dx.doi.org/10.1177/0165551515588669.

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He, Chaobo, Hanchao Li, Xiang Fei, Atiao Yang, Yong Tang, and Jia Zhu. "A topic community-based method for friend recommendation in large-scale online social networks." Concurrency and Computation: Practice and Experience 29, no. 6 (July 21, 2016): e3924. http://dx.doi.org/10.1002/cpe.3924.

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Shao, Shiyun, Yunni Xia, Kaifeng Bai, and Xiaoxin Zhou. "A Quasi-Newton Matrix Factorization-Based Model for Recommendation." International Journal of Web Services Research 20, no. 1 (December 11, 2023): 1–15. http://dx.doi.org/10.4018/ijwsr.334703.

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Solving large-scale non-convex optimization problems is the fundamental challenge in the development of matrix factorization (MF)-based recommender systems. Unfortunately, employing conventional first-order optimization approaches proves to be an arduous endeavor since their curves are very complex. The exploration of second-order optimization methods holds great promise. They are more powerful because they consider the curvature of the optimization problem, which is captured by the second-order derivatives of the objective function. However, a significant obstacle arises when directly applying Hessian-based approaches: their computational demands are often prohibitively high. Therefore, the authors propose AdaGO, a novel quasi-Newton method-based optimizer to meet the specific requirements of large-scale non-convex optimization problems. AdaGO can strike a balance between computational efficiency and optimization performance. In the comparative studies with state-of-the-art MF-based models, AdaGO demonstrates its superiority by achieving higher prediction accuracy.

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Li, Yuxin, Jingyi Wang, Xinyang Wu, Rui Zhou, and Baichuan Xu. "Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems." Theoretical and Natural Science 19, no. 1 (December 8, 2023): 257–64. http://dx.doi.org/10.54254/2753-8818/19/20230568.

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Self-supervised learning (SSL) has been proposed in machine learning projects for its convenience of reducing self-labeled datasets in recent years. However, the implementation of SSL in large-scale recommendation systems has lagged behind the evolution because of their scarce and tailed characteristics. In 2021, an article proposed the use of SSL in recommendation systems to pursue an improvement in the performance of recommender models. This article is built on this previous investigation and aims to further explore the role of SSL in recommendation systems and to investigate an improvement of the models efficiency. To answer research questions, this paper tests three models with different numbers of towers to discover the best performance of the use of SSL in recommender models. Consequently, it is found that implementing SSL on the item side only (two-tower DNNs) produced the best result. Then, when constructing the two-tower DNNs model, this article examines different numbers of negative pairs to change the InfoNCE loss to investigate a tradeoff between the number of positive and negative samples in the performance of the model. As a result, it turns out to be a weak correlation between this ratio and the performance; Hence, it is concluded that the change of the number of positive and negative samples would not necessarily affect the two-tower DNNs model. In our experiential stage, this paper uses a real-world dataset with 100k training samples to testify and compare our results.

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Zheng, Kai, Xianjun Yang, Yilei Wang, Yingjie Wu, and Xianghan Zheng. "Collaborative filtering recommendation algorithm based on variational inference." International Journal of Crowd Science 4, no. 1 (January 31, 2020): 31–44. http://dx.doi.org/10.1108/ijcs-10-2019-0030.

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Purpose The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms. Design/methodology/approach Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods. Findings The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M. Originality/value This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

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Kim, JaeWon, JeongA Wi, SooJin Jang, and YoungBin Kim. "Sequential Recommendations on Board-Game Platforms." Symmetry 12, no. 2 (February 2, 2020): 210. http://dx.doi.org/10.3390/sym12020210.

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The game market is an increasingly large industry. The board-game market, which is the most traditional in the game market, continues to show a steady growth. It is very important for both publishers and players to predict the propensity of users in this huge market and to recommend new games. Despite its importance, no study has been performed on board-game recommendation systems. In this study, we propose a method to build a deep-learning-based recommendation system using large-scale user data of an online community related to board games. Our study showed that new games can be effectively recommended for board-game users based on user big data accumulated for a long time. This is the first study to propose a personalized recommendation system for users in the board-game market and to introduce a provision of new large datasets for board-game users. The proposed dataset shares symmetric characteristics with other datasets and has shown its ability to be applied to various recommendation systems through experiments. Therefore, the dataset and recommendation system proposed in this study are expected to be applied for various studies in the field.

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Pedersen, Rasmus Rex. "Datafication and the push for ubiquitous listening in music streaming." MedieKultur: Journal of media and communication research 36, no. 69 (December 11, 2020): 071–89. http://dx.doi.org/10.7146/mediekultur.v36i69.121216.

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This article discusses Spotify’s approach to music recommendation as dataficationof listening. It discusses the hybrid types of music recommendation that Spotifypresents to users. The article explores how datafication is connected to Spotify’spush for the personalization and contextualization of music recommendationsbased on a combination of the cultural knowledge found in editorial curation andthe potential for large-scale personalization found in algorithmic curation. Thearticle draws on the concept of ubiquitous music and other understandings ofthe affective and functional aspects of music listening as an everyday practice toreflect upon how Spotify’s approach to datafication of listening potentially leads itto prioritize music recommendations that entice users to engage in inattentive andcontinuous listening. In extension to this, the article seeks to contribute with knowledgeabout how the datafication of listening potentially shapes listening practicesand conceptions of relevance and quality in music recommendation.

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Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (November 2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.

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Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

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Zhang, Hailin, Penghao Zhao, Xupeng Miao, Yingxia Shao, Zirui Liu, Tong Yang, and Bin Cui. "Experimental Analysis of Large-Scale Learnable Vector Storage Compression." Proceedings of the VLDB Endowment 17, no. 4 (December 2023): 808–22. http://dx.doi.org/10.14778/3636218.3636234.

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Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality of sparse data in recommendation tasks and the huge volume of corpus in retrieval-related tasks lead to a large memory consumption of the embedding table, which poses a great challenge to the training and deployment of models. Recent research has proposed various methods to compress the embeddings at the cost of a slight decrease in model quality or the introduction of other overheads. Nevertheless, the relative performance of these methods remains unclear. Existing experimental comparisons only cover a subset of these methods and focus on limited metrics. In this paper, we perform a comprehensive comparative analysis and experimental evaluation of embedding compression. We introduce a new taxonomy that categorizes these techniques based on their characteristics and methodologies, and further develop a modular benchmarking framework that integrates 14 representative methods. Under a uniform test environment, our benchmark fairly evaluates each approach, presents their strengths and weaknesses under different memory budgets, and recommends the best method based on the use case. In addition to providing useful guidelines, our study also uncovers the limitations of current methods and suggests potential directions for future research.

Journal articles on the topic 'Large Scale Recommendation'

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