ACM Transactions on Internet Technology最新文献_第2页

Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things 无人机医疗物联网的分布式计算卸载和功率控制

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-03-16 DOI: 10.1145/3652513

Jiakun Gao, Xiaolong Xu, Lianyong Qi, Wanchun Dou, Xiaoyu Xia, Xiaokang Zhou

{"title":"Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things","authors":"Jiakun Gao, Xiaolong Xu, Lianyong Qi, Wanchun Dou, Xiaoyu Xia, Xiaokang Zhou","doi":"10.1145/3652513","DOIUrl":"https://doi.org/10.1145/3652513","url":null,"abstract":"The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving (7.1% ) reduction in energy consumption and (16% ) decrease in average delay.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"15 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Audio-Visual Event Localization using Multi-task Hybrid Attention Networks for Smart Healthcare Systems 利用多任务混合注意力网络为智能医疗系统提供视听事件定位功能

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-03-16 DOI: 10.1145/3653018

Han Liang, Jincai Chen, Fazlullah Khan, Gautam Srivastava, Jiangfeng Zeng

{"title":"Audio-Visual Event Localization using Multi-task Hybrid Attention Networks for Smart Healthcare Systems","authors":"Han Liang, Jincai Chen, Fazlullah Khan, Gautam Srivastava, Jiangfeng Zeng","doi":"10.1145/3653018","DOIUrl":"https://doi.org/10.1145/3653018","url":null,"abstract":"Human perception heavily relies on two primary senses: vision and hearing, which are closely inter-connected and capable of complementing each other. Consequently, various multimodal learning tasks have emerged, with audio-visual event localization (AVEL) being a prominent example. AVEL is a popular task within the realm of multimodal learning, with the primary objective of identifying the presence of events within each video segment and predicting their respective categories. This task holds significant utility in domains such as healthcare monitoring and surveillance, among others. Generally speaking, audio-visual co-learning offers a more comprehensive information landscape compared to single-modal learning, as it allows for a more holistic perception of ambient information, aligning with real-world applications. Nevertheless, the inherent heterogeneity of audio and visual data can introduce challenges related to event semantics inconsistency, potentially leading to incorrect predictions. To track these challenges, we propose a multi-task hybrid attention network (MHAN) to acquire high-quality representation for multimodal data. Specifically, our network incorporates hybrid attention of uni- and parallel cross-modal (HAUC) modules, which consists of a uni-modal attention block and a parallel cross-modal attention block, leveraging multimodal complementary and hidden information for better representation. Furthermore, we advocate for the use of a uni-modal visual task as auxiliary supervision to enhance the performance of multimodal tasks employing a multi-task learning strategy. Our proposed model has been proven to outperform the state-of-the-art results based on extensive experiments conducted on the AVE dataset.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"21 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Market manipulation of Cryptocurrencies: Evidence from Social Media and Transaction Data 加密货币的市场操纵：来自社交媒体和交易数据的证据

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-30 DOI: 10.1145/3643812

Li Wen, Lingfeng Bao, Jiachi Chen, John Grundy, Xin Xia, Xiaohu Yang

{"title":"Market manipulation of Cryptocurrencies: Evidence from Social Media and Transaction Data","authors":"Li Wen, Lingfeng Bao, Jiachi Chen, John Grundy, Xin Xia, Xiaohu Yang","doi":"10.1145/3643812","DOIUrl":"https://doi.org/10.1145/3643812","url":null,"abstract":"The cryptocurrency market cap experienced a great increase in recent years. However, large price fluctuations demonstrate the need for governance structures and identify whether there are market manipulations. In this paper, we conducted three analyses – social media data analysis, blockchain data analysis, and price bubble analysis – to investigate whether market manipulation exists on Bitcoin, Ethereum, and Dogecoin platforms. Social media data analysis aims to find the reasons for the price fluctuations. Blockchain data analysis is used to find the detailed behavior of the manipulators. Price bubble analysis is used to investigate the relation between price fluctuation and manipulators’ behavior. By using the three analyses, we show that market manipulation exists on Bitcoin, Ethereum and Dogecoin. However, market manipulation of Bitcoin is limited, and for most of Bitcoin’s price fluctuations, we found other explanations. The price for Ethereum is most sensitive to technical updates. Technical companies/teams usually hype some new concepts, e.g., ICO, DeFi, which causes a price spike. The price of Dogecoin has a high correlation with Elon Musk’s Twitter activity, which shows influential individuals have the ability to manipulate its prices. Also, the poor monetary liquidity of Dogecoin allows some users to manipulate its price.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"4 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139648306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AI-assisted Blockchain-enabled Smart and Secure E-prescription Management Framework 人工智能辅助区块链智能安全电子处方管理框架

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-23 DOI: 10.1145/3641279

Siva Sai, Vinay Chamola

{"title":"AI-assisted Blockchain-enabled Smart and Secure E-prescription Management Framework","authors":"Siva Sai, Vinay Chamola","doi":"10.1145/3641279","DOIUrl":"https://doi.org/10.1145/3641279","url":null,"abstract":"Traditional medical prescriptions based on physical paper-based documents are prone to manipulation, errors, and unauthorized reproduction due to their format. Addressing the limitations of the traditional prescription system, e-prescription systems have been introduced in several countries. However, e-prescription systems lead to several concerns like the risk of privacy loss, the problem of double-spending prescriptions, lack of interoperability, and single point of failure, all of which need to be addressed immediately. We propose an AI-assisted blockchain-enabled smart and secure e-prescription management framework to address these issues. Our proposed system overcomes the problems of the centralized e-prescription systems and enables efficient consent management to access prescriptions by incorporating blockchain-based smart contracts. Our work incorporates the Umbral proxy re-encryption scheme in the proposed system, avoiding the need for repeated encryption and decryption of the prescriptions when transferred between different entities in the network. In our work, we employ two different machine learning models(Random Forest classifier and LightGBM classifier) to assist the doctor in prescribing medicines. One is a drug recommendation model, which is aimed at providing drug recommendations considering the medical history of the patients and the general prescription pattern for the particular ailment of the patient. We have fine-tuned the SciBERT model for adverse drug reaction detection. The extensive experimentation and results show that the proposed e-prescription framework is secure, scalable, and interoperable. Further, the proposed machine learning models produce results higher than 95%.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"2 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

SDN-enabled Quantized LQR for Smart Traffic Light Controller to Optimize Congestion 用于智能交通灯控制器的 SDN 量化 LQR 可优化拥堵状况

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-16 DOI: 10.1145/3641104

Anuj Sachan, Neetesh Kumar

{"title":"SDN-enabled Quantized LQR for Smart Traffic Light Controller to Optimize Congestion","authors":"Anuj Sachan, Neetesh Kumar","doi":"10.1145/3641104","DOIUrl":"https://doi.org/10.1145/3641104","url":null,"abstract":"Existing intersection management systems, in urban cities, lack in meeting the current requirements of self-configuration, lightweight computing, and software-defined control, which are necessarily required for congested road-lane networks. To satisfy these requirements, this work proposes effective, scalable, multi-input and multi-output, and congestion prevention enabled intersection management system utilizing a software-defined control interface that not only regularly monitors the traffic to prevent congestion for minimizing queue length and waiting time, it also offers a computationally efficient solution in real-time. For effective intersection management, a modified linear-quadratic regulator, i.e., Quantized Linear Quadratic Regulator (QLQR), is designed along with Software-Defined Networking (SDN) enabled control interface to maximize throughput and vehicles speed and minimize queue length and waiting time at the intersection. Experimental results prove that the proposed SDN-QLQR improves the comparative performance in the interval of 24.94% – 49.07%, 35.78% – 68.86%, 36.67% – 59.08%, and 29.94% – 57.87% for various performance metrics, i.e., average queue length, average waiting time, throughput, and average speed respectively.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"10 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139474811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

ADTO: A Trust Active Detecting based Task Offloading Scheme in Edge Computing for Internet of Things ADTO：物联网边缘计算中基于信任主动检测的任务卸载方案

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-12 DOI: 10.1145/3640013

Xuezheng Yang, Zhiwen Zeng, Anfeng Liu, Neal N. Xiong, Shaobo Zhang

{"title":"ADTO: A Trust Active Detecting based Task Offloading Scheme in Edge Computing for Internet of Things","authors":"Xuezheng Yang, Zhiwen Zeng, Anfeng Liu, Neal N. Xiong, Shaobo Zhang","doi":"10.1145/3640013","DOIUrl":"https://doi.org/10.1145/3640013","url":null,"abstract":"In edge computing, Internet of Things (IoT) devices with weak computing power offload tasks to nearby edge servers for execution, so the task completion time can be reduced and delay sensitive tasks can be facilitated. However, if the task is offloaded to malicious edge servers, the system will suffer losses. Therefore, it is significant to identify the trusted edge servers and offload tasks to trusted edge servers, which can improve the performance of edge computing. However, it is still challenging. In this paper, a trust Active Detecting based Task Offloading (ADTO) scheme is proposed to maximize revenue in edge computing. The main innovation points of our work are as follows: (a) The ADTO scheme innovatively proposes a method to actively get trust by trust detection. This method offloads microtasks to edge servers whose trust needs to be identified, and then quickly identifies the trust of edge servers according to the completion of tasks by edge servers. Based on the identification of the trust, tasks can be offloaded to trusted edge servers, so as to improve the success rate of tasks. (b) Although the trust of edge servers can be identified by our detection, it needs to pay a price. Therefore, to maximize system revenue, searching the most suitable number of trusted edge servers for various conditions is transformed into an optimization problem. Finally, theoretical and experimental analysis shows the effectiveness of the proposed strategy, which can effectively identify the trusted and untrusted edge servers. The task offloading strategy based on trust detection proposed in this paper greatly improves the success rate of tasks, compared with the strategy without trust detection, the task success rate is increased by 40.27%, and there is a significant increase in revenue, which fully demonstrates the effectiveness of the strategy.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"28 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139461497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Atrial Fibrillation Detection from Compressed ECG Measurements for Wireless Body Sensor Network 从无线人体传感器网络的压缩心电图测量结果中检测心房颤动

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-10 DOI: 10.1145/3637440

Yongyong Chen, Junxin Chen, Shuang Sun, Jingyong Su, Qiankun Li, Zhihan Lyu

{"title":"Atrial Fibrillation Detection from Compressed ECG Measurements for Wireless Body Sensor Network","authors":"Yongyong Chen, Junxin Chen, Shuang Sun, Jingyong Su, Qiankun Li, Zhihan Lyu","doi":"10.1145/3637440","DOIUrl":"https://doi.org/10.1145/3637440","url":null,"abstract":"Recent years have witnessed an increasing prevalence of wearable devices in the public, where atrial fibrillation (AF) detection is a popular application in these devices. Generally, AF detection is performed on cloud whereas this paper describes an on-device AF detection method. Technically, compressed sensing (CS) is first used for electrocardiograph (ECG) acquisition. Then QRS detection is proposed to be performed directly on the compressed CS measurements, rather than on the reconstructed signals on the powerful cloud server. Based on the extracted QRS information, AF is determined by quantitatively analyzing the (RR, dRR) plot. Databases with ECG samples collected from both medical-level (MIT-BIH afdb) and wearable ECG devices (Physionet Challenge 2017) are introduced for performance validation. The experiment results well demonstrate that our on-device AF detection algorithm can approach the performance of those implemented on the raw signals. Our proposal is suitable for AF screening directly on the wearable devices, without the support of the data center for signal reconstruction and intelligent analysis.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"8 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139413496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Open Set Dandelion Network for IoT Intrusion Detection 用于物联网入侵检测的开放集蒲公英网络

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-09 DOI: 10.1145/3639822

Jiashu Wu, Hao Dai, Kenneth B. Kent, Jerome Yen, Chengzhong Xu, Yang Wang

{"title":"Open Set Dandelion Network for IoT Intrusion Detection","authors":"Jiashu Wu, Hao Dai, Kenneth B. Kent, Jerome Yen, Chengzhong Xu, Yang Wang","doi":"10.1145/3639822","DOIUrl":"https://doi.org/10.1145/3639822","url":null,"abstract":"As Internet of Things devices become widely used in the real world, it is crucial to protect them from malicious intrusions. However, the data scarcity of IoT limits the applicability of traditional intrusion detection methods, which are highly data-dependent. To address this, in this paper we propose the Open-Set Dandelion Network (OSDN) based on unsupervised heterogeneous domain adaptation in an open-set manner. The OSDN model performs intrusion knowledge transfer from the knowledge-rich source network intrusion domain to facilitate more accurate intrusion detection for the data-scarce target IoT intrusion domain. Under the open-set setting, it can also detect newly-emerged target domain intrusions that are not observed in the source domain. To achieve this, the OSDN model forms the source domain into a dandelion-like feature space in which each intrusion category is compactly grouped and different intrusion categories are separated, i.e., simultaneously emphasising inter-category separability and intra-category compactness. The dandelion-based target membership mechanism then forms the target dandelion. Then, the dandelion angular separation mechanism achieves better inter-category separability, and the dandelion embedding alignment mechanism further aligns both dandelions in a finer manner. To promote intra-category compactness, the discriminating sampled dandelion mechanism is used. Assisted by the intrusion classifier trained using both known and generated unknown intrusion knowledge, a semantic dandelion correction mechanism emphasises easily-confused categories and guides better inter-category separability. Holistically, these mechanisms form the OSDN model that effectively performs intrusion knowledge transfer to benefit IoT intrusion detection. Comprehensive experiments on several intrusion datasets verify the effectiveness of the OSDN model, outperforming three state-of-the-art baseline methods by (16.9% ). The contribution of each OSDN constituting component, the stability and the efficiency of the OSDN model are also verified.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"52 11 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139413651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Federated Learning-based Information Leakage Risk Detection for Secure Medical Internet of Things 基于联合学习的安全医疗物联网信息泄漏风险检测

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-09 DOI: 10.1145/3639466

Tingting Wang, Tao Tang, Zhen Cai, Kai Fang, Jinyu Tian, Jianqing Li, Wei Wang, Feng Xia

{"title":"Federated Learning-based Information Leakage Risk Detection for Secure Medical Internet of Things","authors":"Tingting Wang, Tao Tang, Zhen Cai, Kai Fang, Jinyu Tian, Jianqing Li, Wei Wang, Feng Xia","doi":"10.1145/3639466","DOIUrl":"https://doi.org/10.1145/3639466","url":null,"abstract":"The Medical Internet of Things (MIoT) requires extreme information and communication security, particularly for remote consultation systems. MIoT’s integration of physical and computational components creates a seamless network of medical devices providing high-quality care via continuous monitoring and treatment. However, traditional security methods such as cryptography cannot prevent privacy compromise and information leakage caused by security breaches. To solve this issue, this paper proposes a novel Federated Learning Intrusion Detection System (FLIDS). FLIDS combines Generative Adversarial Network (GAN) and Federated Learning (FL) to detect cyber attacks like Denial of Service (DoS), data modification, and data injection using machine learning. FLIDS shows exceptional performance with over 99% detection accuracy and 1% False Positive Rate (FPR). It saves bandwidth by transmitting 3.8 times fewer bytes compared to central data collection. These results prove FLIDS’ effectiveness in detecting and mitigating security threats in Medical Cyber-Physical Systems (MCPS). The paper recommends scaling up FLIDS to use computing resources from multiple mobile devices for better intrusion detection accuracy and efficiency while reducing the burden on individual devices in MIoT.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"54 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139413652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A Novel Cross-Domain Recommendation with Evolution Learning 利用进化学习进行跨域推荐的新方法

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-01-05 DOI: 10.1145/3639567

Yi-Cheng Chen, Wang-Chien Lee

{"title":"A Novel Cross-Domain Recommendation with Evolution Learning","authors":"Yi-Cheng Chen, Wang-Chien Lee","doi":"10.1145/3639567","DOIUrl":"https://doi.org/10.1145/3639567","url":null,"abstract":"In this “info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of on-line digital activities and e-commerce. Several techniques have been widely applied for recommendation systems, but the cold start and sparsity problems remain a major challenge. The cold start problem occurs when generating recommendations for new users and items without sufficient information. Sparsity refers to the problem of having a large amount of users and items but with few transactions or interactions. In this paper, a novel cross-domain recommendation model, Cross-Domain Evolution Learning Recommendation (abbreviated as CD-ELR), is developed to communicate the information from different domains in order to tackle the cold start and sparsity issues by integrating matrix factorization and recurrent neural network. We introduce an evolutionary concept to describe the preference variation of users over time. Furthermore, several optimization methods are developed for combining the domain features for precision recommendation. Experimental results show that CD-ELR outperforms existing state-of-the-art recommendation baselines. Finally, we conduct experiments on several real-world datasets to demonstrate the practicability of the proposed CD-ELR.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":"63 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139376453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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