IEEE Transactions on Computational Social Systems最新文献_第9页

In-Database Feature Extraction to Improve Early Detection of Problematic Online Gambling Behavior 通过数据库内特征提取改进对有问题在线赌博行为的早期检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-04 DOI: 10.1109/TCSS.2024.3406501

Gabriel Stechschulte;Malte Wintner;Matthias Hemmje;Jürg Schwarz;Suzanne Lischer;Michael Kaufmann

{"title":"In-Database Feature Extraction to Improve Early Detection of Problematic Online Gambling Behavior","authors":"Gabriel Stechschulte;Malte Wintner;Matthias Hemmje;Jürg Schwarz;Suzanne Lischer;Michael Kaufmann","doi":"10.1109/TCSS.2024.3406501","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3406501","url":null,"abstract":"This study involves a comprehensive analysis of an anonymized dataset provided by a Swiss online casino that adds to the identification of reliable early indicators for problematic online gambling. Targeting gambling addiction prevention, our objective was to model and evaluate behavioral characteristics that signal early stages of problem gambling. We scrutinized player behaviors against a list of gamblers previously excluded for problematic gambling, using this as our target variable. Our approach combined traditional gambling risk indicators, as outlined in the existing literature, with innovative exploratory feature engineering and feature selection. This involved computing moving aggregates over specific periods to capture nuanced gambling patterns. All features were evaluated by assessing mutual information with the target variable as well as the collinearity of each pairwise combination of features. Based on our data analysis, we found that the total losses in the previous seven days, total deposits in the previous 15 days, total duration played in the previous seven days, stakes (amount bet per game) over the previous seven days, and making a deposit 12 h after a loss (chasing) were the most informative and independent risk indicators. To assess the accuracy of these indicators for early detection of problematic gambling and accordingly for responsible gambling interventions, we combined them in a linear regression model and compared its performance with the casino's currently used model. We found that a binary decision model based on a linear combination of these indicators provided better recall, greater precision, and more timely decisions than the benchmark.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6868-6881"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Frequency-Aware Cross-Modal Interaction for Multimodal Fake News Detection 学习频率感知跨模态交互，实现多模态假新闻检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-04 DOI: 10.1109/TCSS.2024.3415160

Yan Bai;Yanfeng Liu;Yongjun Li

{"title":"Learning Frequency-Aware Cross-Modal Interaction for Multimodal Fake News Detection","authors":"Yan Bai;Yanfeng Liu;Yongjun Li","doi":"10.1109/TCSS.2024.3415160","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3415160","url":null,"abstract":"Recently, fake news detection (FND) is an essential task in the field of social network analysis, and multimodal detection methods that combine text and image have been significantly explored in the last five years. However, the physical features of images that can be clearly shown in the frequency level are often ignored, and thus cross-modal feature extraction and interaction still remain a great challenge when the frequency domain is introduced for multimodal FND. To address this issue, we propose a frequency-aware cross-modal interaction network (FCINet) for multimodal FND in this article. First, a triple-branch encoder with robust feature extraction capacity is proposed to explore the representation of frequency, spatial, and text domains, separately. Then, we design a parallel cross-modal interaction strategy to fully exploit the interdependencies among them to facilitate multimodal FND. Finally, a combined loss function including deep auxiliary supervision and event classification is introduced to improve the generalization ability for multitask training. Extensive experiments and visual analysis on two public real-world multimodal fake news datasets show that the presented FCINet obtains excellent performance and exceeds numerous state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6568-6579"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incentive Mechanism Design Toward a Win–Win Situation for Generative Art Trainers and Artists 生成艺术培训师与艺术家双赢的激励机制设计

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-04 DOI: 10.1109/TCSS.2024.3415631

Haihan Duan;Abdulmotaleb El Saddik;Wei Cai

{"title":"Incentive Mechanism Design Toward a Win–Win Situation for Generative Art Trainers and Artists","authors":"Haihan Duan;Abdulmotaleb El Saddik;Wei Cai","doi":"10.1109/TCSS.2024.3415631","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3415631","url":null,"abstract":"The recent development of generative art, a typical category of artificial intelligence-generated content (AIGC), is essentially beneficial for social good, which can help amateurs to create artwork and improve experts’ efficiency. However, some artists are opposed to generative art technologies due to the copyright infringement and influence of the artists’ way of earning a living, which makes the artists protest against generative art technologies, causing a lose–lose situation. Adversarial attacks against generative model training are potential solutions to address this issue, while the lose–lose situation cannot be improved. To build a win–win situation, a feasible method is to incentivize the artists to actively contribute their artworks to generative model training without influencing their living or infringing copyright, such as data crowdsourcing, but traditional data crowdsourcing methods cannot well fit the generative art area. Therefore, this article builds a blockchain-based trading system for generative model training data collection and generated artwork circulation. Specifically, this article formulates a social welfare maximization problem based on the reverse auction and designs a corresponding incentive mechanism. The conducted theoretical analysis and numerical evaluation demonstrate the effectiveness of the proposed incentive mechanism toward a win–win situation for generative art model trainers and artists.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7528-7540"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperbolic Translation-Based Sequential Recommendation 基于双曲翻译的顺序推荐

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-04 DOI: 10.1109/TCSS.2024.3409711

Yonghong Yu;Aoran Zhang;Li Zhang;Rong Gao;Shang Gao;Hongzhi Yin

{"title":"Hyperbolic Translation-Based Sequential Recommendation","authors":"Yonghong Yu;Aoran Zhang;Li Zhang;Rong Gao;Shang Gao;Hongzhi Yin","doi":"10.1109/TCSS.2024.3409711","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3409711","url":null,"abstract":"The goal of sequential recommendation algorithms is to predict personalized sequential behaviors of users (i.e., next-item recommendation). Learning representations of entities (i.e., users and items) from sparse interaction behaviors and capturing the relationships between entities are the main challenges for sequential recommendation. However, most sequential recommendation algorithms model relationships among entities in Euclidean space, where it is difficult to capture hierarchical relationships among entities. Moreover, most of them utilize independent components to model the user preferences and the sequential behaviors, ignoring the correlation between them. To simultaneously capture the hierarchical structure relationships and model the user preferences and the sequential behaviors in a unified framework, we propose a general hyperbolic translation-based sequential recommendation framework, namely HTSR. Specifically, we first measure the distance between entities in hyperbolic space. Then, we utilize personalized hyperbolic translation operations to model the third-order relationships among a user, his/her latest visited item, and the next item to consume. In addition, we instantiate two hyperbolic translation-based sequential recommendation models, namely Poincaré translation-based sequential recommendation (PoTSR) and Lorentzian translation-based sequential recommendation (LoTSR). PoTSR and LoTSR utilize the Poincaré distance and Lorentzian distance to measure similarities between entities, respectively. Moreover, we utilize the tangent space optimization method to determine optimal model parameters. Experimental results on five real-world datasets show that our proposed hyperbolic translation-based sequential recommendation methods outperform the state-of-the-art sequential recommendation algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7467-7483"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dense Graph Convolutional With Joint Cross-Attention Network for Multimodal Emotion Recognition 用于多模态情感识别的具有联合交叉注意力的密集图卷积网络

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-04 DOI: 10.1109/TCSS.2024.3412074

Cheng Cheng;Wenzhe Liu;Lin Feng;Ziyu Jia

{"title":"Dense Graph Convolutional With Joint Cross-Attention Network for Multimodal Emotion Recognition","authors":"Cheng Cheng;Wenzhe Liu;Lin Feng;Ziyu Jia","doi":"10.1109/TCSS.2024.3412074","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3412074","url":null,"abstract":"Multimodal emotion recognition (MER) has attracted much attention since it can leverage consistency and complementary relationships across multiple modalities. However, previous studies mostly focused on the complementary information of multimodal signals, neglecting the consistency information of multimodal signals and the topological structure of each modality. To this end, we propose a dense graph convolution network (DGC) equipped with a joint cross attention (JCA), named DG-JCA, for MER. The main advantage of the DG-JCA model is that it simultaneously integrates the spatial topology, consistency, and complementarity of multimodal data into a unified network framework. Meanwhile, DG-JCA extends the graph convolution network (GCN) via a dense connection strategy and introduces cross attention to joint model well-learned features from multiple modalities. Specifically, we first build a topology graph for each modality and then extract neighborhood features of different modalities using DGC driven by dense connections with multiple layers. Next, JCA performs cross-attention fusion in intra- and intermodality based on each modality's characteristics while balancing the contributions of various modalities’ features. Finally, subject-dependent and subject-independent experiments on the DEAP and SEED-IV datasets are conducted to evaluate the proposed method. Abundant experimental results show that the proposed model can effectively extract and fuse multimodal features and achieve outstanding performance in comparison with some state-of-the-art approaches.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6672-6683"},"PeriodicalIF":4.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis 通过原型学习和演示增强基于方面的情感分析的隐含情感理解能力

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-03 DOI: 10.1109/TCSS.2024.3368171

Huizhe Su;Xinzhi Wang;Jinpeng Li;Shaorong Xie;Xiangfeng Luo

{"title":"Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis","authors":"Huizhe Su;Xinzhi Wang;Jinpeng Li;Shaorong Xie;Xiangfeng Luo","doi":"10.1109/TCSS.2024.3368171","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3368171","url":null,"abstract":"In the field of social computing, the task of aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence. The absence of explicit opinion words in the implicit aspect sentiment expressions poses a greater challenge for capturing their sentiment features in the reviews from social media. Many recent efforts use dependency trees or attention mechanisms to model the association between the aspect and other contextual words. However, dependency tree-based methods are inefficient in constructing valuable associations for sentiment classification due to the lack of explicit opinion words. In addition, the use of attention mechanisms to obtain global semantic information easily leads to an undesired focus on irrelevant words that may have sentiments but are not directly related to the specific aspect. In this article, we propose a novel prototype-based demonstration (PD) model for the ABSA task, which contains prototype learning and PD stages. In the prototype learning stage, we employ mask-aware attention to capture the global sentiment feature of aspect and learn sentiment prototypes through contrastive learning. This allows us to acquire comprehensive central semantics of the sentiment polarity that contains the implicit sentiment features. In the PD stage, to provide explicit guidance for the latent knowledge within the T5 model, we utilize prototypes similar to the aspect sentiment as the neural demonstration. Our model outperforms others with a 1.68%/0.28% accuracy gain on the Laptop/Restaurant datasets, especially in the ISE slice, showing improvements of 1.17%/0.26%. These results confirm the superiority of our PD-ABSA in capturing implicit sentiment and improving classification performance. This provides a solution for implicit sentiment classification in social computing.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5631-5646"},"PeriodicalIF":4.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual-Coupling-Based Timetable Rescheduling for Heavy-Haul Railways Under Disruptions 中断情况下基于虚拟耦合的重载铁路时刻表重新安排

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-03 DOI: 10.1109/TCSS.2024.3404550

Xiaolan Ma;Min Zhou;Hongwei Wang;Weichen Song;Hairong Dong

{"title":"Virtual-Coupling-Based Timetable Rescheduling for Heavy-Haul Railways Under Disruptions","authors":"Xiaolan Ma;Min Zhou;Hongwei Wang;Weichen Song;Hairong Dong","doi":"10.1109/TCSS.2024.3404550","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3404550","url":null,"abstract":"As the demand for coal and other ore resources increases, the hauling capacity of heavy-haul railways is severely challenged. Virtual coupling technology has gained attention for its ability to improve operational efficiency in bottleneck sections and reduce the time it takes for trains operating on the line to resume normal operation during emergencies. In this article, virtual coupling-based timetable rescheduling method is proposed to reduce the delays under disruptions and improve the line capacity. A mixed-integer linear program (MILP) model that allows trains to be coupled either at departure or by sharing the same arrival and departure line is formulated to reduce the delay time and its propagation range. The strategies of retiming, rearranging tracks, and virtual coupling are adopted to collaboratively optimize the deviation in train schedules and track utilization under disruptions, aiming to enhance the occupancy capacity of arrival and departure lines while simultaneously reducing train delays. A heuristic algorithm utilizing simulated annealing (SA)-particle swarm optimization (PSO) algorithm is developed to generate optimal train coupling and stopping schemes. Numerical experiments are conducted to verify the effectiveness of the proposed model and heuristic algorithm on a real heavy-haul railway configuration. The results demonstrate that our method effectively reduces train delays and minimizes the impact of track utilization on adjacent stations, as well as the repercussions of train delays on subsequent stations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"7045-7054"},"PeriodicalIF":4.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BERT-Based Deceptive Review Detection in Social Media: Introducing DeceptiveBERT 基于bert的社交媒体欺骗性评论检测：引入欺骗性评论

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-03 DOI: 10.1109/TCSS.2024.3403937

Syeda Basmah Hyder;Noshina Tariq;Syed Atif Moqurrab;Muhammad Ashraf;Joon Yoo;Gautam Srivastava

{"title":"BERT-Based Deceptive Review Detection in Social Media: Introducing DeceptiveBERT","authors":"Syeda Basmah Hyder;Noshina Tariq;Syed Atif Moqurrab;Muhammad Ashraf;Joon Yoo;Gautam Srivastava","doi":"10.1109/TCSS.2024.3403937","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3403937","url":null,"abstract":"In recent years, the Internet has facilitated the emergence of social media platforms as significant channels for individuals to express their thoughts and engage in instantaneous interactions. However, the reliance on online reviews has also given rise to deceptive practices, where anonymous spammers generate fake reviews to manipulate the perception of a product. Ensuring the integrity of the online review system requires identifying and mitigating fake reviews. While existing machine learning (ML)- and neural network (NN)-based sentiment analysis methods can detect deceptive reviews, they often suffer from long training times, high computational resource requirements, and memory constraints. This study aims to overcome these limitations by introducing a transformer-based “deceptive bidirectional encoder representations from transformers (DeceptiveBERT) model.” This model utilizes contextual representations to enhance the precision of deceptive review identification. Transfer learning is employed to leverage knowledge from a pre-existing BERT base-uncased word embedding model, enabling efficient feature extraction. The proposed model incorporates a combination of classification layers to categorize reviews into two distinct categories: deceptive and truthful. Additionally, the study addresses the challenge of imbalanced datasets by utilizing three separate datasets and implementing appropriate methodologies for dataset curation. The effectiveness of the DeceptiveBERT model was evaluated through experimentation. The results demonstrate its efficacy, with the model achieving accuracy rates of 75%, 84.79%, and 81.08% on the Ott, YelpNYC, and YelpZip datasets, respectively.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7234-7243"},"PeriodicalIF":4.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Powerball Method With Biased Stochastic Gradient Estimation for Large-Scale Learning Systems 大规模学习系统有偏随机梯度估计的强力球方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-02 DOI: 10.1109/TCSS.2024.3411630

Zhuang Yang

{"title":"The Powerball Method With Biased Stochastic Gradient Estimation for Large-Scale Learning Systems","authors":"Zhuang Yang","doi":"10.1109/TCSS.2024.3411630","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3411630","url":null,"abstract":"The Powerball method, via incorporating a power coefficient into conventional optimization algorithms, has been considered in accelerating stochastic optimization (SO) algorithms in recent years, giving rise to a series of powered stochastic optimization (PSO) algorithms. Although the Powerball technique is orthogonal to the existing accelerated techniques (e.g., the learning rate adjustment strategy) for SO algorithms, the current PSO algorithms take a nearly similar algorithm framework to SO algorithms, where the direct negative result for PSO algorithms is making them inherit low-convergence rate and unstable performance from SO for practical problems. Inspired by this gap, this work develops a novel class of PSO algorithms from the perspective of biased stochastic gradient estimation (BSGE). Specifically, we first explore the theoretical property and the empirical characteristic of vanilla-powered stochastic gradient descent (P-SGD) with BSGE. Second, to further demonstrate the positive impact of BSGE in enhancing the P-SGD type algorithm, we investigate the feature of theory and experiment of P-SGD with momentum under BSGE, where we particularly focus on the effect of negative momentum in P-SGD that is less studied in PSO. Particularly, we prove that the overall complexity of the resulting algorithms matches that of advanced SO algorithms. Finally, large numbers of numerical experiments on benchmark datasets confirm the successful reformation of BSGE in perfecting PSO. This work provides comprehension of the role of BSGE in PSO algorithms, extending the family of PSO algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7435-7447"},"PeriodicalIF":4.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MetaGA: Metalearning With Graph-Attention for Improved Long-Tail Item Recommendation MetaGA：利用图形注意力进行金属学习，改进长尾项目推荐

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-07-02 DOI: 10.1109/TCSS.2024.3411043

Bingjun Qin;Zhenhua Huang;Zhengyang Wu;Cheng Wang;Yunwen Chen

{"title":"MetaGA: Metalearning With Graph-Attention for Improved Long-Tail Item Recommendation","authors":"Bingjun Qin;Zhenhua Huang;Zhengyang Wu;Cheng Wang;Yunwen Chen","doi":"10.1109/TCSS.2024.3411043","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3411043","url":null,"abstract":"The recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a satisfactory recommendation effect. To address the issue, we propose a novel recommendation model called MetaGA based on metalearning. The MetaGA model obtains initial parameters from head items through metalearning and fine-tunes model parameters during the learning process of tail item features. Additionally, it employs a graph convolutional network and attention mechanism to enhance tail data and reduce the difference between head and tail data. Through the above two steps, the model utilizes the abundant data of the head items to address the problem of sparse data of the tail items, resulting in improved recommendation performance. We conducted extensive experiments on three real-world datasets, and the results demonstrate that our proposed MetaGA model significantly outperforms other state-of-the-art baselines for tail item recommendation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6544-6556"},"PeriodicalIF":4.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0