PeerJ Computer Science最新文献

{"title":"Collaborative filtering based on GNN with attribute fusion and broad attention.","authors":"MingXue Liu, Min Wang, Baolei Li, Qi Zhong","doi":"10.7717/peerj-cs.2706","DOIUrl":"10.7717/peerj-cs.2706","url":null,"abstract":"<p><p>Recommender systems based on collaborative filtering (CF) have been a prominent area of research. In recent years, graph neural networks (GNN) based CF models have effectively addressed the limitations of nonlinearity and higher-order feature interactions in traditional recommendation methods, such as matrix decomposition-based methods and factorization machine approaches, achieving excellent recommendation performance. However, existing GNN-based CF models still have two problems that affect performance improvement. First, although distinguishing between inner interaction and cross interaction, these models still aggregate all attributes indiscriminately. Second, the models do not exploit higher-order interaction information. To address the problems above, this article proposes a collaborative filtering method based on GNN with attribute fusion and broad attention, named GNN-A², which incorporates an inner interaction module with self-attention, a cross interaction module with attribute fusion, and a broad attentive cross module. In summary, GNN-A² model performs inner interactions and cross interactions in different ways, then extracts their higher-order interaction information for prediction. We conduct extensive experiments on three benchmark datasets, <i>i.e</i>., MovieLens 1M, Book-crossing, and Taobao. The experimental results demonstrate that our proposed GNN-A² model achieves comparable performance on area under the curve (AUC) metric. Notably, GNN-A² achieves the optimal performance on Normalized Discounted Cumulative Gain at rank 10 (NDCG@10) over three datasets, with values of 0.9506, 0.9137, and 0.1526, corresponding to respective improvements of 0.68%, 1.57%, and 2.14% compared to the state-of-the-art (SOTA) models. The source code and evaluation datasets are available at: https://github.com/LMXue7/GNN-A2.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2706"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Periodic Event Graphs for multivariate time series pattern prediction.","authors":"SoYoung Park, HyeWon Lee, Sungsu Lim","doi":"10.7717/peerj-cs.2717","DOIUrl":"10.7717/peerj-cs.2717","url":null,"abstract":"<p><p>Understanding and predicting outcomes in complex real-world systems necessitates robust multivariate time series pattern analysis. Advanced techniques, such as dynamic graph neural networks, have shown significant efficacy for these tasks. However, existing approaches often overlook the inherent periodicity in data, leading to reduced pattern or event prediction accuracy, especially in periodic time series. We introduce a new method, called dynamic Periodic Event Graphs (PEGs), to tackle this challenge. The proposed method involves time series decomposition to extract seasonal components that capture periodically recurring patterns within the data. It also uses frequency analysis to extract representative periods from each seasonal component. Additionally, motif patterns, which are recurring sub-sequences in the time series data, are extracted. These motifs are used to define event nodes using the representative periods extracted from the seasonal components. By constructing periodic motif pattern-based dynamic bipartite event graphs, we specifically aim to enhance the performance of link prediction tasks, leveraging periodic characteristics in multivariate time series data. Our method has been rigorously tested on multiple periodic multivariate time series datasets, demonstrating over a 5% improvement in link prediction performance for both transductive and inductive scenarios. This demonstrates a substantial enhancement in predictive accuracy and generalization, providing confidence in the technique's effectiveness. Reproducibility is ensured through publicly available source code, enabling future research and applications.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2717"},"PeriodicalIF":3.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance and simulation analysis of 802.11ax OFDMA in contention-driven scenarios.","authors":"Memoona, Sung Won Kim","doi":"10.7717/peerj-cs.2687","DOIUrl":"10.7717/peerj-cs.2687","url":null,"abstract":"<p><p>The 802.11ax standard introduces Orthogonal Frequency Division Multiple Access (OFDMA), shifting the role of access points (APs) in Wi-Fi networks. This shift integrates intricate scheduling logic, assigning coordinator roles to APs for multi-user uplink (MU-UL) transmissions and streamlining downlink traffic flows. These developments require robust network analysis and simulation tools to investigate the trade-offs associated with using OFDMA. In this study, we validate the implementation of OFDMA in ns-3 Wi-Fi module, enhancing flexibility and support for future updates through a redesign process. Previous studies validate the OFDMA implementation in the ns-3 Wi-Fi module by matching the simulation to predictions of analytical models. In this work, we demonstrate that OFDMA performance aligns with analytical predictions through simulation-based performance evaluations using ns-3 in some contention-driven use cases. The proposed system operates in both the uplink (UL) and downlink (DL) directions, implementing two scheduling logics to manage DL traffic flows and coordinate MU-UL transmissions. Simulation time is reduced by introducing parallel computing in the system. This study provides a reliable network analysis and simulation framework that thoroughly examines the trade-offs involved in using OFDMA.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2687"},"PeriodicalIF":3.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filipino sign language alphabet recognition using Persistent Homology Classification algorithm.","authors":"Cristian B Jetomo, Mark Lexter D De Lara","doi":"10.7717/peerj-cs.2720","DOIUrl":"10.7717/peerj-cs.2720","url":null,"abstract":"<p><p>Increasing number of deaf or hard-of-hearing individuals is a crucial problem since communication among and within the deaf population proves to be a challenge. Despite sign languages developing in various countries, there is still lack of formal implementation of programs supporting its needs, especially for the Filipino sign language (FSL). Recently, studies on FSL recognition explored deep networks. Current findings are promising but drawbacks on using deep networks still prevail. This includes low transparency, interpretability, need for big data, and high computational requirements. Hence, this article explores topological data analysis (TDA), an emerging field of study that harnesses techniques from computational topology, for this task. Specifically, we evaluate a TDA-inspired classifier called Persistent Homology Classification algorithm (PHCA) to classify static alphabet signed using FSL and compare its result with classical classifiers. Experiment is implemented on balanced and imbalanced datasets with multiple trials, and hyperparameters are tuned for a comprehensive comparison. Results show that PHCA and support vector machine (SVM) performed better than the other classifiers, having mean Accuracy of 99.45% and 99.31%, respectively. Further analysis shows that PHCA's performance is not significantly different from SVM, indicating that PHCA performed at par with the best performing classifier. Misclassification analysis shows that PHCA struggles to classify signs with similar gestures, common to FSL recognition. Regardless, outcomes provide evidence on the robustness and stability of PHCA against perturbations to data and noise. It can be concluded that PHCA can serve as an alternative for FSL recognition, offering opportunities for further research.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2720"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the prediction of vitamin D deficiency levels using an integrated approach of deep learning and evolutionary computing.","authors":"Ahmed Alzahrani, Muhammad Zubair Asghar","doi":"10.7717/peerj-cs.2698","DOIUrl":"10.7717/peerj-cs.2698","url":null,"abstract":"<p><p>Vitamin D deficiency (VDD) has emerged as a serious global health concern that can lead to far-reaching consequences, including skeletal issues and long-term illness. Classical diagnostic approaches, although effective, often include invasive techniques and lacks to leverage the massive amount of healthcare data. There is an increasing demand for noninvasive prediction approaches for determining the severity of VDD. This work proposes a novel approach to detect VDD levels by combining deep learning techniques with evolutionary computing (EC). Specifically, we employ a hybrid deep learning model that includes convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to predict VDD data effectively. To improve the models effectiveness and guarantee the optimal choice of the features and hyper-parameters, we incorporate evolutionary computing methods, particularly genetic algorithms (GA). The proposed method has been proven effective through a comprehensive assessment on a benchmark dataset, with 97% accuracy, 96% precision, 97% recall, and 96% F1-score. Our approach yielded improved performance, when compared to earlier methods. This research not only push forward predictive healthcare models but also shows the potential of merging deep learning with evolutionary computing to address intricate health-care issues.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2698"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based feature selection and classification for cerebral infarction screening: an experimental study.","authors":"Yang Niu, Xue Tao, Qinyuan Chang, Mingming Hu, Xin Li, Xiaoping Gao","doi":"10.7717/peerj-cs.2704","DOIUrl":"10.7717/peerj-cs.2704","url":null,"abstract":"<p><p>Cerebral infarction screening (CIS) is critical for timely intervention and improved patient outcomes. We investigate the application of machine learning techniques for feature selection and classification of speech and cognitive function assessments to enhance cerebral infarction screening. We analyze a dataset containing 117 patients (95 patients were diagnosed with cerebral infarction, and 54 were identified as lacunar cerebral infarction of them) comprising speech and cognitive function features from patients with lacunar and non-lacunar cerebral infarction, as well as healthy controls. In this article, we present a framework called CIS which comprises a cerebral infarction screening model to identify cerebral infarction from populations and a diagnostic model to classify lacunar infarction, non-lacunar infarction, and healthy controls. Feature selection method, Recursive Feature Elimination with Cross-Validation (RFECV), is employed to identify the most relevant features. Various classifiers, such as support vector machine, K-nearest neighbor, decision tree, random forest, logistic regression, and eXtreme gradient boosting (XGBoost), were evaluated for their performance in binary and ternary classification tasks. The CIS based on XGBoost classifier achieved the highest accuracy of 88.89% in the binary classification task (<i>i.e</i>., distinguishing cerebral infarction from healthy controls) and 77.78% in the ternary classification task (<i>i.e</i>., distinguishing lacunar infarction, non-lacunar infarction, and healthy controls). The selected features significantly contributed to the classification performance, highlighting their potential in differentiating cerebral infarction subtypes. We develop a comprehensive system to effectively assess cerebral infarction subtypes. This study demonstrates the efficacy of machine learning methods in cerebral infarction screening through the analysis of speech and cognitive function features. These findings suggest that incorporating these techniques into clinical practice could improve early detection and diagnosis of cerebral infarction. Further research with larger and more diverse datasets is warranted to validate and extend these results.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2704"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based novel ensemble method with best score transferred-adaptive neuro fuzzy inference system for energy consumption prediction.","authors":"Birce Dağkurs, İsmail Atacak","doi":"10.7717/peerj-cs.2680","DOIUrl":"10.7717/peerj-cs.2680","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Energy consumption predictions for smart homes and cities benefit many from homeowners to energy suppliers, allowing homeowners to understand and manage their future energy consumption, improve energy efficiency, and reduce energy costs. Predictions can help energy suppliers effectively distribute energy on demand. Therefore, from the past to the present, numerous methods have been conducted using collected data, employing both statistical and artificial intelligence (AI)-based approaches, to achieve successful energy consumption predictions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study proposes a deep learning-based novel ensemble (DLBNE) method with the best score transferred-adaptive neuro fuzzy inference system (BST-ANFIS) as a high-performance and robust approach for energy consumption prediction. The proposed method uses deep learning (DL)-based algorithms, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BI-LSTM), and gated recurrent units (GRUs) as base predictors. The BST-ANFIS architecture combines the individual outcomes of these predictors. In order to build a robust and dynamic prediction model, the interaction between the base predictors and the ANFIS architecture is achieved using a best score transfer approach. The performance of the proposed method in energy consumption prediction was verified through five DL methods, five machine learning (ML) methods, and a DL-based weighted average (DLBWA) ensemble method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In experimental studies, the results were obtained from three-stage analyses: fold, average, and periodic performance analyses. In fold analyses, the proposed method, in terms of the root mean square error (RMSE) metric, demonstrated better performance in four folds on the Internet of Things (IoT)-based smart home (IBSH) dataset, two in the homestead city electricity consumption (HCEC) dataset, and two in the individual household power consumption (IHPC) dataset compared to the other methods. In the average performance analyses, it showed significantly higher performance than the other methods in all metrics for the IBSH and IHPC datasets, and in metrics except the mean absolute error (MAE) metric for the HCEC dataset. The performance results in terms of RMSE, MAE, mean square error (MSE), and mean absolute percentage error (MAPE) metrics from these analyses were obtained as 0.001531, 0.001010, 0.0000031, and 0.001573 for the IBSH dataset; 0.025208, 0.005889, 0.001884, and 0.000137 for the HCEC dataset; and 0.013640, 0.006572, 0.000356, and 0.000943 for the IHPC dataset, respectively. The results of the 120-h periodic analyses also showed that the proposed method yielded a better prediction result than the other methods. Furthermore, a comparison of the proposed method with similar studies in the literature revealed that it demonstrated competitive performance in re","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2680"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight-CancerNet: a deep learning approach for brain tumor detection.","authors":"Asif Raza, Muhammad Javed Iqbal","doi":"10.7717/peerj-cs.2670","DOIUrl":"10.7717/peerj-cs.2670","url":null,"abstract":"<p><p>Detecting brain tumors in medical imaging is challenging, requiring precise and rapid diagnosis. Deep learning techniques have shown encouraging results in this field. However, current models require significant computer resources and are computationally demanding. To overcome these constraints, we suggested a new deep learning architecture named Lightweight-CancerNet, designed to detect brain tumors efficiently and accurately. The proposed framework utilizes MobileNet architecture as the backbone and NanoDet as the primary detection component, resulting in a notable mean average precision (mAP) of 93.8% and an accuracy of 98%. In addition, we implemented enhancements to minimize computing time without compromising accuracy, rendering our model appropriate for real-time object detection applications. The framework's ability to detect brain tumors with different image distortions has been demonstrated through extensive tests combining two magnetic resonance imaging (MRI) datasets. This research has shown that our framework is both resilient and reliable. The proposed model can improve patient outcomes and facilitate decision-making in brain surgery while contributing to the development of deep learning in medical imaging.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2670"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive enhanced hypertension detection through ballistocardiograph signals with Mamba model.","authors":"Adi Alhudhaif, Kemal Polat","doi":"10.7717/peerj-cs.2711","DOIUrl":"10.7717/peerj-cs.2711","url":null,"abstract":"<p><p>This study explores using ballistocardiography (BCG), a non-invasive cardiovascular monitoring technique, combined with advanced machine learning and deep learning models for hypertension detection. The motivation behind this research is to develop a non-invasive and efficient approach for long-term hypertension monitoring, facilitating home-based health assessments. A dataset of 128 BCG recordings has been used, capturing body micro-vibrations from cardiac activity. Various classification models, including Mamba Classifier, Transformer, Stacking, Voting, and XGBoost, were applied to differentiate hypertensive individuals from normotensive ones. In this study, integrating BCG signals with deep learning and machine learning models for hypertension detection is distinguished from previous literature by employing the Mamba deep learning architecture and Transformer-based models. Unlike conventional methods in literature, this study enables more effective analysis of time-series data with the Mamba architecture, capturing long-term signal dependencies and achieving higher accuracy rates. In particular, the combined use of Mamba architecture and the Transformer model's signal processing capabilities represents a novel approach not previously seen in the literature. While existing studies on BCG signals typically rely on traditional machine learning algorithms, this study aims to achieve higher success rates in hypertension detection by integrating signal processing and deep learning stages. The Mamba Classifier outperformed other models, achieving an accuracy of 95.14% and an AUC of 0.9922 in the 25% hold-out validation. Transformer and Stacking models also demonstrated strong performance, while the Voting and XGBoost models showed comparatively lower results. When combined with artificial intelligence techniques, the findings indicate the potential of BCG signals in providing non-invasive, long-term hypertension detection. The results suggest that the Mamba Classifier is the most effective model for this dataset. This research underscores the potential of BCG technology for continuous home-based health monitoring, providing a feasible alternative to traditional methods. Future research should aim to validate these findings with larger datasets and explore the clinical applications of BCG for cardiovascular disease monitoring.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2711"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autoregressive models for session-based recommendations using set expansion.","authors":"Tianhao Yu, Xianghong Zhou, Xinrong Deng","doi":"10.7717/peerj-cs.2734","DOIUrl":"10.7717/peerj-cs.2734","url":null,"abstract":"<p><p>With the rapid growth of internet technologies, session-based recommendation systems have emerged as a key paradigm in delivering personalized recommendations by capturing users' dynamic and short-term preferences. Traditional methods predominantly rely on modeling the sequential order of user interactions, deep learning approaches like recurrent neural networks and Transformer architectures. However, these sequence-based models often struggle in scenarios where the order of interactions is ambiguous or unreliable, limiting their real-world applicability. To address this challenge, we propose a novel session-based recommendation model, Deep Set Session-based Recommendation (DSETRec), which approaches the problem from a set-based perspective, eliminating dependence on the interaction sequence. By conceptualizing session data as unordered sets, our model captures the coupling relationships and co-occurrence patterns between items, enhancing prediction accuracy in settings where sequential information is either unavailable or noisy. The model is implemented using a deep autoregressive framework that iteratively masks known elements within a session, predicting and reconstructing additional items based on set data characteristics. Extensive experiments on benchmark datasets show that DSETRec achieves outperforms state-of-the-art baselines. DSETRec achieves a 13.2% and 11.85% improvement in P@20 and MRR@20, respectively, over its sequence-based variant on Yoochoose. Additionally, DSETRec generalizes effectively across both further short and long sessions. These results highlight the robustness of the set-based approach in capturing unordered interaction patterns and adapting to diverse session lengths. This finding provides a foundation for developing more flexible and generalized session-based recommendation systems.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2734"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}