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Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review

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Multimedia Tools and Applications 2025年第5期84卷 2293-2325页

作者： Jangid, Manisha Kumar, Rakesh Department of Computer Science & Engineering Central University of Haryana Haryana India

Recommendation systems (RS) have become prevalent across different domains including music, e-commerce, e-learning, entertainment, and social media to address the issue of information overload. While traditional RS approaches have achieved significant success in delivering recommendations, they still face issues including sparse data, diversity, cold start, and long tail problem. The emergence of deep learning as a prominent and extensively studied topic has shown significant potential in addressing these challenges in RS. Deep learning captures intricate patterns of interaction and precisely reflects user preferences, allowing for encoding complex abstractions in data representation and enhancing information processing capabilities. This paper provides an extensive survey of the existing literature on recommendation systems. We will begin by providing a foundational understanding of the core concepts and terminology of recommendation systems and significance of deep learning. Secondly, we talk about the original studies being conducted on deep learning methods and solutions to address "Cold start and long tail" challenges in recommendation. Thirdly, we examine the potential future directions of research pertaining to deep learning-based recommender systems (DLRS). Our review provides valuable insights for both researchers and practitioners in using deep learning to address challenges in recommendation and in developing effective and efficient recommendation systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Numerical simulation for the initial state of avalanche in polydisperse particle systems

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Chinese Physics B 2024年第2期33卷 405-412页

作者：韩韧李亭迟志鹏杨晖李然 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.

关键词： avalanche initial state polydisperse particle systems propagation

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An efficient crack detection and leakage monitoring in liquid metal pipelines using a novel BRetN and TCK-LSTM techniques

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Multimedia Tools and Applications 2024年 1-29页

作者： Sankarasubramanian, Praveen Computer Science and Engineering RMD Research Labs Chennai India

Nowadays, the pipeline system has the safest, most economical, and most efficient means of transporting petroleum products and other chemical fluids. But, the faults in pipelines cause resource wastage and environmental pollution. Most of the existing works focused either on the surface Crack Detection (CD) or Leakage Detection (LD) of pipes with limited features. Hence, efficient crack detection and leakage monitoring are proposed based on the Acoustic Emission (AE) signal and AE image features using a new Berout Retina Net (BRetN) and Tent Chaotic Kaiming-centric Long Short Term Memory (TCK-LSTM) methodologies. The process initiates from the gathering of input data, followed by preprocessing. Then, the cracks are detected by utilizing Berout Retina Net (BRetN), and the features of AE signals are retrieved. On the other hand, the AE signal is transformed into an AE image using Continuous Wavelet Transform (CWT). Further, the AE image features are extracted, followed by the integration of both the AE signal and AE image features. Further, the optimal features are chosen by using Gorilla Troops Optimizer (GTO). Eventually, the TCK-LSTM model is used for detecting the leakage level of the pipeline. The experimental outcomes illustrated that the proposed framework detected crack and leakage levels with 98.14% accuracy, 95.37% precision, and 98.84% specificity when analogizing over the existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wavelet transforms

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Deep learning approaches and biomarkers in medical diagnosis

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Recent Patents on engineering 2024年第3期18卷 13-26页

作者： Malik, Pardeep Singh, Sukhdip Computer Science and Engineering Department DCRUST Haryana Sonipat India

The size of the medical information system is growing gradually. Due to this, traditional data analysis for extracting helpful information for any disease has become inefficient in providing accurate real-time valid information. Traditional data mining and statistical learning techniques, which lack sufficient domain knowledge on a complicatedly colossal amount of data, need to stop adopting new advances in deep learning technologies. Thus, the use of sophisticated machines with in-depth understanding and analysis capability is vital to provide real-time information in detecting and diagnosing diseases in the healthcare system. In this paper, we study recent deep learning approaches which are capable of working on high-dimensional and multi-dimensional data. These approaches have been deployed to identify the root cause of various diseases like Cancer, Lung Diseases, Heart Diseases, Diabetes, Hepatitis, Alzheimer’s, Dengue, Parkinson’s, etc. In this study, our key contributions are: 1. A decent overview of the deep learning techniques. 2. Several modalities in the diagnosis of various diseases. 3. Set out recent trends in various applications of deep learning in healthcare, some of which are analysis and diagnosis of medical images, precision medicine, drug discovery, predictive analysis to support clinical decisions, and sustainable public health. 4. Several deep learning approaches with their performance are described in detail. 5. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially the availability of the patient’s data for training deep learning models on a particular disease is comparatively much less than required. © 2024 Bentham science Publishers.

关键词： Diagnosis

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Road Surface Analysis through Machine Learning Techniques

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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Implementation of ANN and response surface method for dual-fuel CI engine optimization and prediction using water infusion and biofuel

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Multimedia Tools and Applications 2025年 1-43页

作者： Pramanik, Sabyasachi Department of Computer Science & Engineering Haldia Institute of Technology Haldia India

In this study, tests were done to see what would happen if hydrogen (H2) and lemon grass oil (LO) were used for a lone-cylinder compression ignition engine as a partial diesel replacement. After starting the trial with pure diesel, the engine was modified to run on two fuels by adding H2 through the inlet pipe and LO as direct injection (DI) gasoline. In this study, the energy fraction of H2 ranged from 5 to 10%. A 3% volume mixture of water (W) and diesel-LO mixtures was used to study how NOX emissions were affected. Using a common back- transmission method, an Artificial Neural Network model was created to forecast the correlation between engine output reactions and input variables (load, LO, and H2). The goal of response surface methodology is for improve the motor input settings for low pollution and high heat efficiency. The trials were designed using response surface methodology (RSM), and the optimization procedure was conducted using RSM. An artificial neural network (ANN) was used to predict the test engine’s emission patterns and performance. The results showed that ANN and RSM were both very good and accurate modeling strategies. When 10% H2 + LO25 was used, the BTE rose to 31%, up from 20% for clean LO and 28% for 12% H2 + LO25 + 2%W means. Compared to clean LO operation, the cylinder pressure raised in 15% H2 mode by up to 63 psi. The measured value for the HRR, under H2 premixed combustion framework was 81 k Joule/CA deg in 10% H2, that is significantly greater than the value for every LO processes. The highest NOX release for 10% H2 + LO 25% was discovered at 690 ppm peak load, while it dropped to 645 ppm in gasoline that had been emulsified in water. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gasoline

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An ensemble based approach for violence detection in videos using deep transfer learning

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Multimedia Tools and Applications 2025年第12期84卷 11001-11025页

作者： Kaur, Gurmeet Singh, Sarbjeet Department of Computer Science and Engineering UIET Panjab University Chandigarh India

The detection of violence in videos has become an extremely valuable application in real-life situations, which aim to maintain and protect people’s safety. Despite the complexities inherent in videos and the abrupt nature of violent actions, the field has seen several approaches, yet achieving consistent performance remains elusive, especially with advanced real-life datasets. Presenting a solution, the paper proposes a Bagging ensemble based approach comprising three pretrained models integrated with stacked Long Short-Term Memory (LSTM) to enhance individual model performance. This ensemble approach is rigorously analyzed on two publicly accessible datasets, RLVS and RWF-2000, providing remarkable accuracy (96.6%, 92.7%) and F1-scores (96.6%, 93.0%). Additionally, a cross-dataset analysis demonstrates the model’s ability to generalize across diverse datasets. Furthermore, a study of ablation highlighting the efficacy and optimal selection of components in augmenting the proposed ensemble’s efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Phrase-level emotion intensity detection of text using lexicon-based unit circle and pipelined neural networks approaches

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Neural Computing and Applications 2025年第5期37卷 3151-3166页

作者： Patil, Bhuvaneshwari Ajit Vadlakunta, Vishnu Vardhan Guruvu, Poojaa Pranathi Mathew, Meenu Prakash, Jay Computer Science and Engineering National Institute of Technology Calicut Kozhikode India

Emotions have a significant impact on how people make decisions. Due to its potential applications in various fields, emotion intensity detection has attracted a lot of attention recently. Several methods have been proposed in the past using natural language processing to recognize emotions from the text. These methods include the keyword-based approach, the lexicon-based approach and the machine learning approaches. Nevertheless, there were a few limitations with the lexicon-based unit circle approach, as it focuses on detecting emotion intensity at the word-level. This research proposes an integrated model by considering the advantages of the lexicon-based unit circle and pipelined neural networks approaches. The model is trained on a large corpus of text data. The proposed approach aims to determine emotion intensity, analyzing text at the phrase-level, thereby achieving higher performance in terms of accuracy and F1-score compared to other models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural networks

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Evaluation of phone posterior probabilities for pathology detection in speech data using deep learning models

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International Journal of Speech Technology 2025年第1期28卷 99-116页

作者： Farazi, Sahar Shekofteh, Yasser Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Voice pathology detection (VPD) aims to accurately identify voice impairments by analyzing speech signals. This study proposes models based on deep learning (DL) for binary classification to distinguish between healthy and pathological voices, with a unique focus on the integration of phone posterior probabilities (PPPs as phonetic-based features) alongside Mel-frequency cepstral coefficients (MFCCs as acoustic-based features) for input models. By incorporating PPPs as supplementary information, we investigate the model’s performance across spontaneous, sustained vowel, and read speech datasets, addressing the gap in comparing these speech types for VPD. Our results highlight that PPPs significantly enhance classification accuracy, particularly for read speech and spontaneous speech data types. Using the AVFAD database, we show that the proposed CNN-based model achieves its highest performance on spontaneous speech, with an accuracy of approximately 87% on test data and 93% on validation data. This study emphasizes the impact of PPPs as phonetic-based features in VPD tasks and clarifies which types of speech benefit most from their inclusion, paving the way for more refined models in this field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： AVFAD dataset Convolutional neural network Deep learning Phone posterior probabilities Spontaneous speech Voice pathology detection

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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science China(Information sciences) 2024年第10期67卷 244-260页

作者： Xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong XIE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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