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CRBFT:A Byzantine Fault-Tolerant Consensus Protocol Based on Collaborative Filtering Recommendation for Blockchains
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作者 Xiangyu Wu Xuehui Du +3 位作者 Qiantao Yang Aodi Liu Na Wang Wenjuan Wang 《Computers, Materials & Continua》 SCIE EI 2024年第7期1491-1519,共29页
Blockchain has been widely used in finance,the Internet of Things(IoT),supply chains,and other scenarios as a revolutionary technology.Consensus protocol plays a vital role in blockchain,which helps all participants t... Blockchain has been widely used in finance,the Internet of Things(IoT),supply chains,and other scenarios as a revolutionary technology.Consensus protocol plays a vital role in blockchain,which helps all participants to maintain the storage state consistently.However,with the improvement of network environment complexity and system scale,blockchain development is limited by the performance,security,and scalability of the consensus protocol.To address this problem,this paper introduces the collaborative filtering mechanism commonly used in the recommendation system into the Practical Byzantine Fault Tolerance(PBFT)and proposes a Byzantine fault-tolerant(BFT)consensus protocol based on collaborative filtering recommendation(CRBFT).Specifically,an improved collaborative filtering recommendation method is designed to use the similarity between a node’s recommendation opinions and those of the recommender as a basis for determining whether to adopt the recommendation opinions.This can amplify the recommendation voice of good nodes,weaken the impact of cunningmalicious nodes on the trust value calculation,andmake the calculated resultsmore accurate.In addition,the nodes are given voting power according to their trust value,and a weight randomelection algorithm is designed and implemented to reduce the risk of attack.The experimental results show that CRBFT can effectively eliminate various malicious nodes and improve the performance of blockchain systems in complex network environments,and the feasibility of CRBFT is also proven by theoretical analysis. 展开更多
关键词 Blockchain CONSENSUS byzantine fault-tolerant collaborative filtering TRUST
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Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations 被引量:1
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作者 Muhammad Ibrahim Imran Sarwar Bajwa +3 位作者 Nadeem Sarwar Haroon Abdul Waheed Muhammad Zulkifl Hasan Muhammad Zunnurain Hussain 《Computers, Materials & Continua》 SCIE EI 2023年第3期5301-5317,共17页
Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep lear... Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services. 展开更多
关键词 Neural sentiment classification user product attention deep collaborative filtering multivariate rating artificial intelligence
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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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A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications 被引量:2
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作者 Wenmin Lin Min Zhu +4 位作者 Xinyi Zhou Ruowei Zhang Xiaoran Zhao Shigen Shen Lu Sun 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第3期897-910,共14页
Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and... Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods. 展开更多
关键词 deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron
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Prediction of(n,2n)reaction cross-sections of long-lived fission products based on tensor model
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作者 Jia-Li Huang Hui Wang +7 位作者 Ying-Ge Huang Er-Xi Xiao Yu-Jie Feng Xin Lei Fu-Chang Gu Long Zhu Yong-Jing Chen Jun Su 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第10期208-221,共14页
Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reac... Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste. 展开更多
关键词 (n 2n)Reaction cross-section Tensor model Machine learning Collaborative filtering algorithm Selective transmutation
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Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder
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作者 S.Abinaya K.Uttej Kumar 《Computers, Materials & Continua》 SCIE EI 2024年第2期2269-2286,共18页
A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer prefe... A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures. 展开更多
关键词 Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering
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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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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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A novel similarity measurement approach considering intrinsic user groups in collaborative filtering
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作者 顾梁 杨鹏 董永强 《Journal of Southeast University(English Edition)》 EI CAS 2015年第4期462-468,共7页
To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups( SMCUG) is proposed considering the social information of users. The approach co... To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups( SMCUG) is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering. 展开更多
关键词 SIMILARITY user group CLUSTER collaborative filtering
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A New Time-Aware Collaborative Filtering Intelligent Recommendation System 被引量:6
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作者 Weijin Jiang Jiahui Chen +4 位作者 Yirong Jiang Yuhui Xu Yang Wang Lina Tan Guo Liang 《Computers, Materials & Continua》 SCIE EI 2019年第8期849-859,共11页
Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces projec... Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project attributes.Then,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more accurate.In the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large weight.The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality. 展开更多
关键词 Fuzzy clustering time weight attenuation function Collaborative filtering method recommendation algorithm
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Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems 被引量:3
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作者 YAO Yu ZHU Shanfeng CHEN Xinmeng 《Wuhan University Journal of Natural Sciences》 CAS 2006年第5期1086-1090,共5页
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider... In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users' ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage. 展开更多
关键词 Kendall correlation collaborative filtering algorithms recommender systems positive correlation
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Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering 被引量:2
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作者 Mengwei Hou Rong Wei +2 位作者 Tiangang Wang Yu Cheng Buyue Qian 《Computers, Materials & Continua》 SCIE EI 2018年第7期137-149,共13页
Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issu... Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients. 展开更多
关键词 Medical recommendation privacy preserving neighborhood-based collaborative filtering differential privacy
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A Novel Collaborative Filtering Algorithm and its Application for Recommendations in E-Commerce 被引量:2
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作者 Jie Zhang Juan Yang +3 位作者 Li Wang Yizhang Jiang Pengjiang Qian Yuan Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第3期1275-1291,共17页
With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vas... With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data.In this study,we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce.This study has two main innovations.First,we propose a mechanismthat embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item.Second,we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization.We compared the proposed method with several state-of-the-art alternatives on real datasets.The experimental results show that our proposed method outperforms the prevailing approaches. 展开更多
关键词 Collaborative filtering temporal behavior probability matrix factorization
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Fuzzy collaborative filtering with multiple agents 被引量:2
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作者 黄芹华 欧阳为民 《Journal of Shanghai University(English Edition)》 CAS 2007年第3期290-295,共6页
Automated collaborative filtering has become a popular technique for reducing information overload. We have developed a new method for recommending items using multiple agents. The agents were established by employing... Automated collaborative filtering has become a popular technique for reducing information overload. We have developed a new method for recommending items using multiple agents. The agents were established by employing the fuzzy C-means clustering technique. We employ these agents collaborating each other to get recommendation for users. The results were evaluated by using MovieLens movie's rating data. It is shown that the algorithm is an effective metrics in collaborative filtering. 展开更多
关键词 collaborative filtering multiple agents fuzzy C-means
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Time-Ordered Collaborative Filtering for News Recommendation 被引量:6
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作者 XIAO Yingyuan AI Pengqiang +2 位作者 Ching-Hsien Hsu WANG Hongya JIAO Xu 《China Communications》 SCIE CSCD 2015年第12期53-62,共10页
Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recom... Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis. 展开更多
关键词 similarity collaborative compute recommendation filtering users hundreds Collaborative Recommendation interested
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Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment 被引量:1
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作者 Thavavel Vaiyapuri 《Computers, Materials & Continua》 SCIE EI 2021年第7期487-503,共17页
The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means t... The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before.However,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services.Thus,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service.Most of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality recommendations.Inspired by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations.The proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel.Next,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features.Finally,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks.We conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation metrics.Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance.Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems. 展开更多
关键词 Neural collaborative filtering cold-start problem data sparsity multilayer perception generalized matrix factorization autoencoder deep learning ensemble learning top-K recommendations
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Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering 被引量:1
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作者 Qusai Y.Shambour Abdelrahman H.Hussein +1 位作者 Qasem M.Kharma Mosleh M.Abualhaj 《Computer Systems Science & Engineering》 SCIE EI 2022年第1期113-125,共13页
Requirements engineering(RE)is among the most valuable and critical processes in software development.The quality of this process significantly affects the success of a software project.An important step in RE is requ... Requirements engineering(RE)is among the most valuable and critical processes in software development.The quality of this process significantly affects the success of a software project.An important step in RE is requirements elicitation,which involves collecting project-related requirements from different sources.Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements.However,the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately.Recommender systems have been widely recognized as an effective solution to such problem.Accordingly,this study proposes an effective hybrid content-based collaborative filtering recommendation approach.The proposed approach will support project stake-holders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories.The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recom-mendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems. 展开更多
关键词 Requirements engineering recommender systems requirements elicitation collaborative filtering content-based filtering
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Improved Collaborative Filtering Recommendation Based on Classification and User Trust 被引量:3
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作者 Xiao-Lin Xu Guang-Lin Xu 《Journal of Electronic Science and Technology》 CAS CSCD 2016年第1期25-31,共7页
When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes ... When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation. 展开更多
关键词 Collaborative filtering credibility of ratings evaluation on user trust item classification similarity metric
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Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model 被引量:1
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作者 Feng Zhang Hua Ma +1 位作者 Lei Peng Lanhua Zhang 《International Journal of Technology Management》 2017年第3期79-81,共3页
The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is... The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed. 展开更多
关键词 Random walk algorithm collaborative filtering model cloud computing system recommendation algorithm
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A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern 被引量:1
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作者 Loc Nguyen 《Journal of Data Analysis and Information Processing》 2016年第3期124-131,共8页
Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application... Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response. 展开更多
关键词 Aware-Context Collaborative filtering Logistic Regression Model
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