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Journal articles on the topic 'Data Sparsity Problem'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Xue, Andy Yuan, Jianzhong Qi, Xing Xie, Rui Zhang, Jin Huang, and Yuan Li. "Solving the data sparsity problem in destination prediction." VLDB Journal 24, no. 2 (2014): 219–43. http://dx.doi.org/10.1007/s00778-014-0369-7.

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Wan, Xinyue, Bofeng Zhang, Guobing Zou, and Furong Chang. "Sparse Data Recommendation by Fusing Continuous Imputation Denoising Autoencoder and Neural Matrix Factorization." Applied Sciences 9, no. 1 (2018): 54. http://dx.doi.org/10.3390/app9010054.

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In recent years, although deep neural networks have yielded immense success in solving various recognition and classification problems, the exploration of deep neural networks in recommender systems has received relatively less attention. Meanwhile, the inherent sparsity of data is still a challenging problem for deep neural networks. In this paper, firstly, we propose a new CIDAE (Continuous Imputation Denoising Autoencoder) model based on the Denoising Autoencoder to alleviate the problem of data sparsity. CIDAE performs regular continuous imputation on the missing parts of the original data

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Zahedi, A., and M. H. Kahaei. "Frequency Estimation of Irregularly Sampled Data Using a Sparsity Constrained Weighted Least-Squares Approach." Engineering, Technology & Applied Science Research 3, no. 1 (2013): 368–72. http://dx.doi.org/10.48084/etasr.187.

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In this paper, a new method for frequency estimation of irregularly sampled data is proposed. In comparison with the previous sparsity-based methods where the sparsity constraint is applied to a least-squares fitting problem, the proposed method is based on a sparsity constrained weighted least-squares problem. The resulting problem is solved in an iterative manner, allowing the usage of the solution obtained at each iteration to determine the weights of the least-squares fitting term at the next iteration. Such an appropriate weighting of the least-squares fitting term enhances the performanc

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Liu, Li Min, Peng Xiang Zhang, Le Lin, and Zhi Wei Xu. "Research of Data Sparsity Based on Collaborative Filtering Algorithm." Applied Mechanics and Materials 462-463 (November 2013): 856–60. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.856.

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During the traditional collaborative filtering recommendation algorithm be impacted by itself data sparseness problem. It can not provide accurate recommendation result. In this paper, Using traditional collaborative filtering algorithm and the concept of similar level, take advantage of the idea of data populating to solve sparsity problem, then using the Weighted Slope One algorithm to recommend calculating. Experimental results show that the improved algorithm solved the problem of the recommendation results of low accuracy because of the sparse scoring matrix, and it improved the algorithm

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Li, Zhixian. "Exploring the Path of Innovative Development of Traditional Culture under Big Data." Computational Intelligence and Neuroscience 2022 (August 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/7715851.

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Chinese traditional culture is the treasure of our cultural field. In the new era, it is of great significance to give traditional culture a new life and vitality. The term “big data” is hotly debated all over the world, while the development of big data is gradually occupying all aspects of the society that people are compatible with society. It is an imperative initiative to build a cultural data system by making use of big data technology, and cultural big data can make Chinese traditional culture release more vitality. This paper analyzes the new characteristics of traditional culture deve

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Yu, Chengyuan, and Linpeng Huang. "CluCF: a clustering CF algorithm to address data sparsity problem." Service Oriented Computing and Applications 11, no. 1 (2016): 33–45. http://dx.doi.org/10.1007/s11761-016-0191-8.

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Kieu, Hai Dang, Hongchuan Yu, Zhuorong Li, and Jian Jun Zhang. "Locally weighted PCA regression to recover missing markers in human motion data." PLOS ONE 17, no. 8 (2022): e0272407. http://dx.doi.org/10.1371/journal.pone.0272407.

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“Missing markers problem”, that is, missing markers during a motion capture session, has been raised for many years in Motion Capture field. We propose the locally weighted principal component analysis (PCA) regression method to deal with this challenge. The main merit is to introduce the sparsity of observation datasets through the multivariate tapering approach into traditional least square methods and develop it into a new kind of least square methods with the sparsity constraints. To the best of our knowledge, it is the first least square method with the sparsity constraints. Our experimen

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Guo, Yuhong. "Convex Subspace Representation Learning from Multi-View Data." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 387–93. http://dx.doi.org/10.1609/aaai.v27i1.8565.

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Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We ﬁrst formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual norms, we then show a convex min-max dual formulation with a sparsity inducing trace norm can be obtained. We develop a proximal bundle optimization algorithm to globally solve the min-max optimization pr

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Pan, Weike, Evan Xiang, Nathan Liu, and Qiang Yang. "Transfer Learning in Collaborative Filtering for Sparsity Reduction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 230–35. http://dx.doi.org/10.1609/aaai.v24i1.7578.

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Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrat

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Su, Chang, Yue Yu, Xianzhong Xie, and Yukun Wang. "Data Sensitive Recommendation Based On Community Detection." Foundations of Computing and Decision Sciences 40, no. 2 (2015): 143–59. http://dx.doi.org/10.1515/fcds-2015-0010.

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Abstract Collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF. Data sensitive similarity is combined with Pearson similarity to calculate the similarity between users. α is the control parameter. A predicted rating mechanism is used to solve data sparsity problem and to obtain more accurate recommendation. Both user-user similarity and item-item similarity are considere

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Shanmuga Sundari, P., and M. Subaji. "Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment." International Journal of Information Technology & Decision Making 19, no. 02 (2020): 385–412. http://dx.doi.org/10.1142/s0219622020500108.

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Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to r

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Dell’Aversano, Angela, Giovanni Leone, and Raffaele Solimene. "Comparing Two Approaches for Point-Like Scatterer Detection." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/139235.

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Many inverse scattering problems concern the detection and localisation of point-like scatterers which are sparsely enclosed within a prescribed investigation domain. Therefore, it looks like a good option to tackle the problem by applying reconstruction methods that are properly tailored for such a type of scatterers or that naturally enforce sparsity in the reconstructions. Accordingly, in this paper we compare the time reversal-MUSIC and the compressed sensing. The study develops through numerical examples and focuses on the role of noise in data and mutual coupling between the scatterers.

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Chen, Xiang, Junxin Chen, Xiaoqin Lian, and Weimin Mai. "Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations." Applied Sciences 12, no. 14 (2022): 6882. http://dx.doi.org/10.3390/app12146882.

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Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characterist

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Gholami, Ali. "Residual statics estimation by sparsity maximization." GEOPHYSICS 78, no. 1 (2013): V11—V19. http://dx.doi.org/10.1190/geo2012-0035.1.

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Residual statics estimation in complex areas is one of the main challenging problems in seismic data processing. It is well known that the result of this processing step has a profound effect on the quality of final reconstructed image. A novel method is presented to compensate for surface-consistent residual static corrections based on sparsity maximization, which has proved to be a powerful tool in the analysis and processing of signals and related problems. The method is based on the hypothesis that residual static time shift represents itself by noise-like features in the Fourier or curvel

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Nasiri, Mahdi, Behrouz Minaei, and Zeinab Sharifi. "Adjusting data sparsity problem using linear algebra and machine learning algorithm." Applied Soft Computing 61 (December 2017): 1153–59. http://dx.doi.org/10.1016/j.asoc.2017.05.042.

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Ren, Xiaozhen, and Yuying Jiang. "Spatial Domain Terahertz Image Reconstruction Based on Dual Sparsity Constraints." Sensors 21, no. 12 (2021): 4116. http://dx.doi.org/10.3390/s21124116.

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Terahertz time domain spectroscopy imaging systems suffer from the problems of long image acquisition time and massive data processing. Reducing the sampling rate will lead to the degradation of the imaging reconstruction quality. To solve this issue, a novel terahertz imaging model, named the dual sparsity constraints terahertz image reconstruction model (DSC-THz), is proposed in this paper. DSC-THz fuses the sparsity constraints of the terahertz image in wavelet and gradient domains into the terahertz image reconstruction model. Differing from the conventional wavelet transform, we introduce

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Ifada, Noor, and Richi Nayak. "A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 387–99. http://dx.doi.org/10.22266/ijies2021.0228.36.

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The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to repre

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Junxi, Yang, Zongshui Wang, and Chong Chen. "GCN-MF: A graph convolutional network based on matrix factorization for recommendation." Innovation & Technology Advances 2, no. 1 (2024): 14–26. http://dx.doi.org/10.61187/ita.v2i1.30.

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With the increasing development of information technology and the rise of big data, the Internet has entered the era of information overload. While users enjoy the convenience brought by big data to their daily lives, they also face more and more information filtering and selection problems. In this context, recommendation systems have emerged, and existing recommendation systems cannot effectively deal with the problem of data sparsity. Therefore, this paper proposes a graph convolutional network based on matrix factorization for recommendation. The embedding layer uses matrix factorization i

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Huang, Weiming, Baisong Liu, and Zhaoliang Wang. "A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem." Electronics 13, no. 2 (2024): 419. http://dx.doi.org/10.3390/electronics13020419.

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Paper recommendation systems are important for alleviating academic information overload. Such systems provide personalized recommendations based on implicit feedback from users, supplemented by their subject information, citation networks, etc. However, such recommender systems face problems like data sparsity for positive samples and uncertainty for negative samples. In this paper, we address these two issues and improve upon them from the perspective of metric learning. The algorithm is modeled as a push–pull loss function. For the positive sample pull-out operation, we introduce a context

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Mat Nawi, Rosmamalmi, Chee Xuan Yui, Shahrul Azman Mohd Noah, Noryusliza Abdullah, and Norfaradilla Wahid. "A Cross-Domain Linked Open Data-Enabled in Collaborative Group Recommender System." Journal of Advanced Research in Applied Sciences and Engineering Technology 62, no. 3 (2024): 89–101. https://doi.org/10.37934/araset.62.3.89101.

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A new search paradigm is continuously evolving, with users' perspectives on information searching shifting from searching for information to receiving information. One of the new methods of receiving information is through recommender systems (RS). RS have proven to be successful in many traditional domains including tourism and books. The group recommender system (GRS) and individual RS challenges are triggered by the limited and incomplete number of user-item ratings. The data sparsity problem emerges because of this incompleteness. Data sparsity in a group has a negative impact on the quali

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Mustapha, Maidawa, Y. Dutse A., Ahmad Aminu, Ya'u Gital Abdulsalam, and Zahraddeen Yakubu Ismail. "An Improvised Business Intelligence Recommender System using Data Mining Algorithm." An Improvised Business Intelligence Recommender System using Data Mining Algorithm 8, no. 11 (2023): 12. https://doi.org/10.5281/zenodo.10297550.

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AI allows for a higher quality of recommendation than can be achieved by conventional recommendation methods. This has ushered in a new era for recommender systems, creating advanced observations of the relationship between users and items, presented an expanded understanding of demographic, textural, virtual, and contextual data as well as more intricate data representations. However, the challenge for the recommendation systems is to solve the problems of sparsity, scalability, and cold start. The existing capsule networks take times in training making it a slow algorithm. Also, ignoring the

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Choi, Keunho, Yongmoo Suh, and Donghee Yoo. "Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem." International Journal of Computers Communications & Control 11, no. 5 (2016): 631. http://dx.doi.org/10.15837/ijccc.2016.5.2152.

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Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usual

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Yin*, Yuyu, Haoran Xu, Tingting Liang*, Manman Chen, Honghao Gao, and Antonella Longo. "Leveraging Data Augmentation for Service QoS Prediction in Cyber-physical Systems." ACM Transactions on Internet Technology 21, no. 2 (2021): 1–25. http://dx.doi.org/10.1145/3425795.

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With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact th

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Yu, Jiangni, Lixiang Li, and Yixian Yang. "Topology Identification of Coupling Map Lattice under Sparsity Condition." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/303454.

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Coupling map lattice is an efficient mathematical model for studying complex systems. This paper studies the topology identification of coupled map lattice (CML) under the sparsity condition. We convert the identification problem into the problem of solving the underdetermined linear equations. Thel1norm method is used to solve the underdetermined equations. The requirement of data characters and sampling times are discussed in detail. We find that the high entropy and small coupling coefficient data are suitable for the identification. When the measurement time is more than 2.86 times sparsit

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Ho-Nguyen, Nam, and Fatma Kılınç-Karzan. "Technical Note—Dynamic Data-Driven Estimation of Nonparametric Choice Models." Operations Research 69, no. 4 (2021): 1228–39. http://dx.doi.org/10.1287/opre.2020.2077.

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Choice models are prevalent in several application areas, and their nonparametric estimation was introduced to alleviate unreasonable assumptions in traditional parametric models. Existing literature focuses only on the static observational setting where all of the observations are given up front and lacks algorithms that provide explicit convergence rate guarantees or an a priori analysis for the model accuracy versus sparsity trade-off on the actual estimated model returned. In contrast, in “Dynamic Data-Driven Estimation of Nonparametric Choice Models,” Ho-Nguyen and Kılınç-Karzan focus on

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Guo, Jingfeng, Chao Zheng, Shanshan Li, Yutong Jia, and Bin Liu. "BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation." Mathematics 10, no. 17 (2022): 3042. http://dx.doi.org/10.3390/math10173042.

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The current graph-neural-network-based recommendation algorithm fully considers the interaction between users and items. It achieves better recommendation results, but due to a large amount of data, the interaction between users and items still suffers from the problem of data sparsity. To address this problem, we propose a method to alleviate the data sparsity problem by retaining user–item interactions while fully exploiting the association relationships between items and using side-information enhancement. We constructed a “twin-tower” model by combining a user–item training model and an it

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Kwon, Hyeong-Joon, and Kwang Seok Hong. "Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering." International Journal of Distributed Sensor Networks 9, no. 8 (2013): 847965. http://dx.doi.org/10.1155/2013/847965.

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Cheng, Guang Hua. "An Effective Hybrid Collaborative Recommendation Algorithm for Alleviating Data Sparsity." Applied Mechanics and Materials 39 (November 2010): 535–39. http://dx.doi.org/10.4028/www.scientific.net/amm.39.535.

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Every day there is lots of information obtained via the Internet. The problem of information overload is becoming increasingly serious, and we have all experienced the feeling of being overwhelmed. Many researchers and practitioners more attention on building a suitable tool that can help users conserve resources and services that are wanted. Personalized recommendation systems are used to make recommendations for the user invisible elements get to their preferences, which differ in the position, a user from one another in order to provide information based. The paper presented a personalized

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Khoshsokhan, S., R. Rajabi, and H. Zayyani. "DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 145–50. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-145-2017.

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Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm,

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Zeng, Fanfan, Hongwei Du, Jiaquan Jin, Jinzhang Xu, and Bensheng Qiu. "Compressed Sensing MRI via Extended Anisotropic and Isotropic Total Variation." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1066–75. http://dx.doi.org/10.1166/jmihi.2019.2702.

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Compressed sensing (CS) is a technique to reconstruct images from undersampling data, reducing the scanning time of magnetic resonance imaging (MRI). It utilizes the sparsity of images in some transform domains. Total variation (TV) has been applied to enforce sparsity. However, traditional TV based on the l1-norm is not the most direct way to induce sparsity, and it cannot offer a sufficiently sparse representation. Since the lp-norm (0< p < 1) promotes the sparsity better than that of the l1-norm, we propose two extended TV algorithms based on the lp-norm: anisotropic and isotropic tot

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Yuan, Jinfeng, and Li Li. "Recommendation Based on Trust Diffusion Model." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/159594.

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Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship

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Zhu, Hong, Li-Zhi Liao, and Michael K. Ng. "Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality." Neural Computation 30, no. 12 (2018): 3281–308. http://dx.doi.org/10.1162/neco_a_01140.

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We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and

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Hanhela, Matti, Olli Gröhn, Mikko Kettunen, Kati Niinimäki, Marko Vauhkonen, and Ville Kolehmainen. "Data-Driven Regularization Parameter Selection in Dynamic MRI." Journal of Imaging 7, no. 2 (2021): 38. http://dx.doi.org/10.3390/jimaging7020038.

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In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are

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Boualaoui, Bouchra, Ahmed Zellou, and Mouna Berquedich. "Knowledge Graph-Based Recommender Systems to Mitigate Data Sparsity: A Systematic Literature Review." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 03 (2025): 115–40. https://doi.org/10.3991/ijim.v19i03.49427.

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Recommender systems (RSs) have become important tools in the modern lifestyle; they have been integrated into all domains, spanning from entertainment (music, films, etc.) to more sensitive fields such as security and health care. Their success does not mean that they are ideal or flawless; quite the opposite, RSs suffer from plenty of drawbacks and challenges that need to be resolved. Data sparsity is a common problem in recommender systems; it has been of top interest among researchers. Numerous approaches from different perspectives have been proposed to mitigate it, including knowledge gra

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Li, Xiu Juan, and He Biao Yang. "Application and Research on Distributed Collaborative Filtering Recommendation Algorithm Based on Hadoop." Applied Mechanics and Materials 713-715 (January 2015): 1615–21. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1615.

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Coupled with exponential expansion of the data, efficient computing of existing recommendation algorithm has become an important issue, and the traditional collaborative filtering recommendation algorithm also exist the problem of sparsity. Based on the detailed analysis, the article introduce Hadoop platform into improved collaborative filtering recommendation algorithm, the improved collaborative filtering recommendation algorithm solve the problem of data sparsity, MapReduce parallel computing of recommendation also solve the promble of computational efficiency. In the experiments, the comp

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Kwon, Hyeong-Joon, and Kwang-Seok Hong. "Method to Improve Data Sparsity Problem of Collaborative Filtering Using Latent Attribute Preference." Journal of Korean Society for Internet Information 14, no. 5 (2013): 59–67. http://dx.doi.org/10.7472/jksii.2013.14.5.59.

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Wen, Ying, Le Zhang, and Lili Hou. "Discriminant Sparsity Preserving Analysis for Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 02 (2016): 1656003. http://dx.doi.org/10.1142/s0218001416560036.

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Sparse subspace learning has drawn more and more attentions recently, however, most of them are unsupervised and unsuitable for classification tasks. In this paper, a new discriminant sparsity preserving analysis (DSPA) method by integrating sparse reconstructive weighting into Fisher criterion is proposed for face recognition. We first get sparsity preserving space spanned by the eigenvectors of sparsity preserving projections (SPP). Then, the optimal projection can be obtained by solving an eigenvalue and eigenvector problem of the between-class scatter matrix in sparsity preserving space. T

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Natarajan, Senthilselvan, Subramaniyaswamy Vairavasundaram, Sivaramakrishnan Natarajan, and Amir H. Gandomi. "Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data." Expert Systems with Applications 149 (July 2020): 113248. http://dx.doi.org/10.1016/j.eswa.2020.113248.

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Bi, Bo, Bo Han, Weimin Han, Jinping Tang, and Li Li. "Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation." Computational and Mathematical Methods in Medicine 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/286161.

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Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angula

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Zhou, Junkai, Bo Jiang, Jie Yang, et al. "Service Discovery Method Based on Knowledge Graph and Word2vec." Electronics 11, no. 16 (2022): 2500. http://dx.doi.org/10.3390/electronics11162500.

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Mashup is a new type of application that integrates multiple Web APIs. For mashup application development, the quality of the selected APIs is particularly important. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. To solve these problems in current service discovery

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Zhang, Xue, and Wangxin Xiao. "Active semi-supervised framework with data editing." Computer Science and Information Systems 9, no. 4 (2012): 1513–32. http://dx.doi.org/10.2298/csis120202045z.

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In order to address the insufficient training data problem, many active semi-supervised algorithms have been proposed. The self-labeled training data in semi-supervised learning may contain much noise due to the insufficient training data. Such noise may snowball themselves in the following learning process and thus hurt the generalization ability of the final hypothesis. Extremely few labeled training data in sparsely labeled text classification aggravate such situation. If such noise could be identified and removed by some strategy, the performance of the active semi-supervised algorithms sh

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Riyadi, Daffa Barin Tizard, and Z. K. A. Baizal. "Collaborative Filtering with Dimension Reduction Technique and Clustering for E-Commerce Product." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 376. http://dx.doi.org/10.30865/mib.v7i1.5538.

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The rapid development of internet users over the last decade has led to an increase in the use of electronic commerce (e-commerce). The existence of a recommender system influences the success of e-commerce. Collaborative Filtering (CF) is one of the most frequently used recommender system methods. However, in real cases, sparsity problems generally occur. This is generally caused because only a small number of users give ratings to items. In this study, we propose the combination of clustering and dimension reduction methods on the Amazon Review Data to overcome the sparsity problem. The clus

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Moriyoshi, Kenshin, Hiroki Shibata, and Yasufumi Takama. "Generation of Rating Matrix Based on Rational Behaviors of Users." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 1 (2024): 129–40. http://dx.doi.org/10.20965/jaciii.2024.p0129.

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This paper proposes a method to generate a synthetic rating matrix based on user’s rational behavior, with the aim of generating a large-scale rating matrix at low cost. Collaborative filtering is one of the major techniques for recommender systems, which is widely used because it can recommend items using only a history of ratings given to the items by users. However, collaborative filtering has some problems such as the cold-start problem and the sparsity problem, both of which are caused by the shortage of ratings in a database (rating matrix). This problem is particularly serious for servi

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Yang, Xing-Yao, Feng Xu, Jiong Yu, Zi-Yang Li, and Dong-Xiao Wang. "Graph Neural Network-Guided Contrastive Learning for Sequential Recommendation." Sensors 23, no. 12 (2023): 5572. http://dx.doi.org/10.3390/s23125572.

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Sequential recommendation uses contrastive learning to randomly augment user sequences and alleviate the data sparsity problem. However, there is no guarantee that the augmented positive or negative views remain semantically similar. To address this issue, we propose graph neural network-guided contrastive learning for sequential recommendation (GC4SRec). The guided process employs graph neural networks to obtain user embeddings, an encoder to determine the importance score of each item, and various data augmentation methods to construct a contrast view based on the importance score. Experimen

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Huang, Jiaquan, Zhen Jia, and Peng Zuo. "Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space." Mathematical Modelling and Control 3, no. 1 (2023): 39–49. http://dx.doi.org/10.3934/mmc.2023004.

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<abstract><p>Collaborative filtering (CF) algorithm is one of the most widely used recommendation algorithms in recommender systems. However, there is a data sparsity problem in the traditional CF algorithm, which may reduce the recommended efficiency of recommender systems. This paper proposes an improved collaborative filtering personalized recommendation (ICF) algorithm, which can effectively improve the data sparsity problem by reducing item space. By using the k-means clustering method to secondarily extract the similarity information, ICF algorithm can obtain the similarity i

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Menkin, A. V. "Development of a Music Recommender System Based on Content Metadata Processing." Vestnik NSU. Series: Information Technologies 17, no. 3 (2019): 43–60. http://dx.doi.org/10.25205/1818-7900-2019-17-3-43-60.

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Music recommender systems (MRS) help users of music streaming services to find interesting music in the music catalogs. The sparsity problem is an essential problem of MRS research. It refers to the fact that user usually rates only a tiny part of items. As a result, MRS often has not enough data to make a recommendation. To solve the sparsity problem, in this paper, a new approach that uses related items’ ratings is proposed. Hybrid MRS based on this approach is described. It uses tracks, albums, artists, genres normalized ratings along with information about relations between items of differ

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Bevacqua, Martina T., and Roberta Palmeri. "Qualitative Methods for the Inverse Obstacle Problem: A Comparison on Experimental Data." Journal of Imaging 5, no. 4 (2019): 47. http://dx.doi.org/10.3390/jimaging5040047.

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Qualitative methods are widely used for the solution of inverse obstacle problems. They allow one to retrieve the morphological properties of the unknown targets from the scattered field by avoiding dealing with the problem in its full non-linearity and considering a simplified mathematical model with a lower computational burden. Very many qualitative approaches have been proposed in the literature. In this paper, a comparison is performed in terms of performance amongst three different qualitative methods, i.e., the linear sampling method, the orthogonality sampling method, and a recently in

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Zang, Tingpeng, Guangrui Wen, and Zhifen Zhang. "Robust Estimation of the Unbalance of Rotor Systems Based on Sparsity Control of the Residual Model." Shock and Vibration 2018 (August 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/6508695.

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The vibration signals of rotating machinery are frequently disturbed by background noise and external disturbances because of the equipment’s particular working environment. Thus, robustness has become one of the most important problems in identifying the unbalance of rotor systems. Based on the observation that external disturbance of the unbalance response often displays sparsity compared with measured vibration data, we present a new robust method for identifying the unbalance of rotor systems based on model residual sparsity control. The residual model is composed of two parts: one part ta

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Zeng, Junhua, Yuning Qiu, Yumeng Ma, Andong Wang, and Qibin Zhao. "A Novel Tensor Ring Sparsity Measurement for Image Completion." Entropy 26, no. 2 (2024): 105. http://dx.doi.org/10.3390/e26020105.

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As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronecker basis representation of a given piece of data in the matrix format. Nevertheless, in practical scenarios, much of the data are intrinsically multi-dimensional, and thus, using a matrix format for data representation will inevitably yield sub-optimal outcomes. Tensor decomposition (TD), as a high-order generalization of matrix decomposition, has

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More, Tejashree, and Prof Surekha Kohle. "Recommendation System Using Matrix Factorization." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 355–59. http://dx.doi.org/10.22214/ijraset.2022.46615.

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Abstract: In today’s world, people are flooded with a lot of information, and no. of choices are overwhelming. For example, in any online shopping platform such as Amazon, if we search for a particular product, thousands of results appear and it becomes very difficult to select an item from vast pool of options. The growth of digital information and the number of users over the Internet has created a potential problem of information overload. The recommendation system solves this problem by searching through a large volume of data and providing personalized content to the user. This paper desc

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