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An Adaptive Program Recommendation System for Multi-User Sharing Environment
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作者 Sun Shiyun Hu Zhengying +1 位作者 Wei Xin Zhou Liang 《China Communications》 SCIE CSCD 2024年第6期112-128,共17页
More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and ... More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme. 展开更多
关键词 ADAPTIVE EXPLOITATION LinUCB MULTIUSER recommendation system
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ARCHER:a ReRAM-based accelerator for compressed recommendation systems
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作者 Xinyang SHEN Xiaofei LIAO +3 位作者 Long ZHENG Yu HUANG Dan CHEN Hai JIN 《Frontiers of Computer Science》 SCIE EI CSCD 2024年第5期147-160,共14页
Modern recommendation systems are widely used in modern data centers.The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms... Modern recommendation systems are widely used in modern data centers.The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and memory.ReRAM-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are stored.However,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM profits.Therefore,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance loss.In this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource constraints.First,we make a full analysis of the computation pattern and access pattern on the decomposed table.Based on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate operations.Based on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource utilization.Under the unified computation and mapping strategy,we can coordinatethe inter-processing elements pipeline.The evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively. 展开更多
关键词 s recommendation system RERAM processing-in-memory embedding layer
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Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective
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作者 Qingyuan Ji Xiaoyue Wen +2 位作者 Junchen Jin Yongdong Zhu Yisheng Lv 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第10期2043-2058,共16页
Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal ... Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field. 展开更多
关键词 recommendation system traffic control traffic perception traffic prediction
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Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System
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作者 Ameni Ellouze Nesrine Kadri +1 位作者 Alaa Alaerjan Mohamed Ksantini 《Computers, Materials & Continua》 SCIE EI 2024年第4期351-372,共22页
Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell t... Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women. 展开更多
关键词 Human physical activities smartphone sensors deep learning distributed monitoring recommendation system uncertainty HEALTHY CALORIES
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Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System
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作者 Weiming Huang Baisong Liu Zhaoliang Wang 《Computers, Materials & Continua》 SCIE EI 2024年第6期4449-4469,共21页
In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniq... In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques. 展开更多
关键词 Collaborative filtering citation networks variational inference poisson factorization tag recommendation
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Recommendation System Based on Perceptron and Graph Convolution Network
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作者 Zuozheng Lian Yongchao Yin Haizhen Wang 《Computers, Materials & Continua》 SCIE EI 2024年第6期3939-3954,共16页
The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combinatio... The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms. 展开更多
关键词 recommendation system graph convolution network attention mechanism multi-layer perceptron
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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems
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作者 Sang-min Lee Namgi Kim 《Computers, Materials & Continua》 SCIE EI 2024年第2期1897-1914,共18页
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ... Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets. 展开更多
关键词 Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems
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Video Recommendation System Using Machine-Learning Techniques
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作者 Meesala Sravani Ch Vidyadhari S Anjali Devi 《Journal of Harbin Institute of Technology(New Series)》 CAS 2024年第4期24-33,共10页
In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through learning.In this cycle,Video recommendation is fini... In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through learning.In this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different clients.The expectation depends on past evaluations,history,interest,IMDB rating,and so on.This can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific time.The required datasets for the video are taken from Grouplens.This recommender framework is executed by utilizing Python Programming Language.For building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the examples.For that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information focuses.The last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings. 展开更多
关键词 video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence
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Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics
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作者 Dinesh Kalla Nathan Smith +1 位作者 Fnu Samaah Kiran Polimetla 《Journal of Data Analysis and Information Processing》 2024年第1期76-88,共13页
This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of co... This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3]. 展开更多
关键词 Azure Data Lake U-SQL Author recommendation system Power BI Microsoft Academic Big Data Word Embedding
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Recommendation Algorithm Integrating CNN and Attention System in Data Extraction 被引量:1
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作者 Yang Li Fei Yin Xianghui Hui 《Computers, Materials & Continua》 SCIE EI 2023年第5期4047-4063,共17页
With the rapid development of the Internet globally since the 21st century,the amount of data information has increased exponentially.Data helps improve people’s livelihood and working conditions,as well as learning ... With the rapid development of the Internet globally since the 21st century,the amount of data information has increased exponentially.Data helps improve people’s livelihood and working conditions,as well as learning efficiency.Therefore,data extraction,analysis,and processing have become a hot issue for people from all walks of life.Traditional recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low performance.To solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network framework.Through the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ones.Meanwhile,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher.The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding.It obtains data name features through a convolution kernel.Finally,the top pooling layer obtains the length vector.The attention system layer obtains the characteristics of the data type.Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage.The proposed algorithm shows excellent accuracy and robustness. 展开更多
关键词 Data extraction recommendation algorithm CNN algorithm attention model
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Content-Based Movie Recommendation System Using MBO with DBN 被引量:1
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作者 S.Sridhar D.Dhanasekaran G.Charlyn Pushpa Latha 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3241-3257,共17页
The content-basedfiltering technique has been used effectively in a variety of Recommender Systems(RS).The user explicitly or implicitly provides data in the Content-Based Recommender System.The system collects this da... The content-basedfiltering technique has been used effectively in a variety of Recommender Systems(RS).The user explicitly or implicitly provides data in the Content-Based Recommender System.The system collects this data and creates a profile for all the users,and the recommendation is generated by the user profile.The recommendation generated via content-basedfiltering is provided by observing just a single user’s profile.The primary objective of this RS is to recommend a list of movies based on the user’s preferences.A con-tent-based movie recommendation model is proposed in this research,which recommends movies based on the user’s profile from the Facebook platform.The recommendation system is built with a hybrid model that combines the Mon-arch Butterfly Optimization(MBO)with the Deep Belief Network(DBN).For feature selection,the MBO is utilized,while DBN is used for classification.The datasets used in the experiment are collected from Facebook and MovieLens.The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations.Eachfile is com-pared with features that prove the features will support movie recommendations.The proposed model’s mean absolute error(MAE)and root-mean-square error(RMSE)values are 0.716 and 0.915,and its precision and recall are 97.35 and 96.60 percent,respectively.Extensive tests have demonstrated the advantages of the proposed method in terms of MAE,RMSE,Precision,and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm(FCM-BAT),Collaborativefiltering with k-NN and the normalized discounted cumulative gain method(CF-kNN+NDCG),User profile correlation-based similarity(UPCSim),and Deep Autoencoder. 展开更多
关键词 Movie recommendation monarch butterfly optimization deep belief network facebook movielens deep learning
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A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems 被引量:1
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作者 Samin Poudel Marwan Bikdash 《Journal of Computer and Communications》 2023年第3期110-130,共21页
Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspe... Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects. 展开更多
关键词 recommender system Collaborative Filtering Aspect based recommendation recommendation system Framework Aspect Sentiments
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Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study
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作者 Francisco JoséGarcía-Penlvo Andrea Vázquez-Ingelmo Alicia García-Holgado 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1023-1051,共29页
The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interes... The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms. 展开更多
关键词 SLR systematic literature review artificial intelligence machine learning algorithm recommendation HEURISTICS explainability
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Graph Convolutional Network-Based Repository Recommendation System
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作者 Zhifang Liao Shuyuan Cao +3 位作者 Bin Li Shengzong Liu Yan Zhang Song Yu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期175-196,共22页
GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community infor... GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate. 展开更多
关键词 Repository recommendation graph convolutional network open-source software GitHub
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Hybrid Recommender System Using Systolic Tree for Pattern Mining
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作者 S.Rajalakshmi K.R.Santha 《Computer Systems Science & Engineering》 SCIE EI 2023年第2期1251-1262,共12页
A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking in... A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved. 展开更多
关键词 recommender systems hybrid recommender systems frequent pattern mining collaborativefiltering systolic tree river formation dynamics particle swarm optimization
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IoT-Deep Learning Based Activity Recommendation System
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作者 Sharmilee Kannan R.U.Anitha +1 位作者 M.Divayapushpalakshmi K.S.Kalaivani 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期2001-2016,共16页
The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,... The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical settings.The Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health care.Healthcare Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have increased.The existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports.Patients’health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies.This research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it accordingly.The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures.Through a healthcare DSS,this framework is clever enough to detect and analyze a patient’s data.Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care.The data reported in this study demonstrate the notion.This device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals. 展开更多
关键词 Deep learning IOT healthcare system activity recommender system body sensors
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A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems
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作者 Son-Lam VU Quang-Hung LE 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期471-483,共13页
Recommender systems are similar to an informationfiltering system that helps identify items that best satisfy the users’demands based on their pre-ference profiles.Context-aware recommender systems(CARSs)and multi-cr... Recommender systems are similar to an informationfiltering system that helps identify items that best satisfy the users’demands based on their pre-ference profiles.Context-aware recommender systems(CARSs)and multi-criteria recommender systems(MCRSs)are extensions of traditional recommender sys-tems.CARSs have integrated additional contextual information such as time,place,and so on for providing better recommendations.However,the majority of CARSs use ratings as a unique criterion for building communities.Meanwhile,MCRSs utilize user preferences in multiple criteria to better generate recommen-dations.Up to now,how to exploit context in MCRSs is still an open issue.This paper proposes a novel approach,which relies on deep learning for context-aware multi-criteria recommender systems.We apply deep neural network(DNN)mod-els to predict the context-aware multi-criteria ratings and learn the aggregation function.We conduct experiments to evaluate the effect of this approach on the real-world dataset.A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness. 展开更多
关键词 recommender systems CONTEXT-AWARE MULTI-CRITERIA deep learning deep neural network
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Automatic Clustering of User Behaviour Profiles for Web Recommendation System
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作者 S.Sadesh Osamah Ibrahim Khalaf +3 位作者 Mohammad Shorfuzzaman Abdulmajeed Alsufyani K.Sangeetha Mueen Uddin 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3365-3384,共20页
Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved perform... Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%. 展开更多
关键词 Data mining web mining process search engine web-page recommendation ACCURACY
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A multilayer network diffusion-based model for reviewer recommendation
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作者 黄羿炜 徐舒琪 +1 位作者 蔡世民 吕琳媛 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第3期700-717,共18页
With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to d... With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes. 展开更多
关键词 reviewer recommendation multilayer network network diffusion model recommender systems complex networks
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Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation
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作者 Ruijin Xue Shuang Feng Qi Wang 《Computers, Materials & Continua》 SCIE EI 2024年第8期3107-3122,共16页
Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recomm... Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations. 展开更多
关键词 News recommendation DIVERSITY ACCURACY data augmentation
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