检索结果-南通市图书馆

recommender system for Configuration Management Process of Entrepreneurial Software Designing Firms

Computers, Materials & Continua 2021年第5期67卷 2373-2391页

作者： Muhammad Wajeeh Uz Zaman Yaser Hafeez Shariq Hussain Haris Anwaar Shunkun Yang Sadia Ali Aaqif Afzaal Abbasi Oh-Young Song University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Electronics and Telecommunication EngineeringUniversity of Engineering and TechnologyLahore54000Pakistan School of Reliability and systems Engineering Beihang UniversityBeijing100191China Department of Software Sejong UniversitySeoul05006South Korea

The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers *** problem is addressed by the software development industry by software product line(SPL)practices that employ feature ***,optimal feature selection based on user requirements is a challenging ***,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited *** this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and *** proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and *** system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management ***,a list of features is suggested to the client according to their needs and *** experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and *** provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction *** results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system ***,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated ***,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the

关键词： Feature selection recommender system software reuse configuration management

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A New recommender system Using Context Clustering Based on Matrix Factorization Techniques

引用

Chinese Journal of Electronics 2016年第2期25卷 334-340页

作者： ZHENG Xiaoyao LUO Yonglong SUN Liping CHEN Fulong College of Territorial Resources and Tourism Anhui Normal University School of Mathematics and Computer Science Anhui Normal University

recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings *** with several ma jor existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.

关键词： recommender system Matrix factorization Contextual information Clustering

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Improve the Collaborative Filtering recommender system Performance by Trust Network Construction

引用

Chinese Journal of Electronics 2016年第3期25卷 418-423页

作者： DU Yongping DU Xiaoyan HUANG Liang College of Computer Science Beijing University of Technology

Data sparseness brings significant challenges to the research of recommender systems. It becomes more severe for neighborhood-based collaborative *** introduce the trust relation computing of the sociology field. Instead of the traditional similarity computing method, the trust degree is integrated for the nearest neighbor selection. The trust network is constructed by the expansion of different path length, and the trust value between the users can be obtained by the trust transmission rules. To verify the effectiveness of our method, we give the experiments on different techniques for rating prediction,including Pearson based method, the User position similarity(UPS) based method and the trust with Pearson and UPS. We also give the t-test result. The implementation of the experiment on the Epinions data set shows that the proposed method can improve the system performance significantly.

关键词： Collaborative filtering Trust computing recommender system

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

APPLET: a privacy-preserving framework for location-aware recommender system

引用

Science China(Information Sciences) 2017年第9期60卷 5-20页

作者： Xindi MA Hui LI Jianfeng MA Qi JIANG Sheng GAO Ning XI Di LU School of Computer Science and Technology Xidian University School of Cyber Engineering Xidian University School of Information Central University of Finance and Economics

Location-aware recommender systems that use location-based ratings to produce recommendations have recently experienced a rapid development and draw significant attention from the research ***, current work mainly focused on high-quality recommendations while underestimating privacy issues,which can lead to problems of privacy. Such problems are more prominent when service providers, who have limited computational and storage resources, leverage on cloud platforms to fit in with the tremendous number of service requirements and users. In this paper, we propose a novel framework, namely APPLET, for protecting user privacy information, including locations and recommendation results, within a cloud environment. Through this framework, all historical ratings are stored and calculated in ciphertext, allowing us to securely compute the similarities of venues through Paillier encryption, and predict the recommendation results based on Paillier,commutative, and comparable encryption. We also theoretically prove that user information is private and will not be leaked during a recommendation. Finally, empirical results over a real-world dataset demonstrate that our framework can efficiently recommend POIs with a high degree of accuracy in a privacy-preserving manner.

关键词： recommender system location-based service homomorphic encryption privacy-preserving frame work collaborative filtering

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

A correlative denoising autoencoder to model social influence for top-N recommender system

引用

Frontiers of Computer Science 2020年第3期14卷 31-43页

作者： Yiteng PAN Fazhi HE Haiping YU School of Computer Science Wuhan UniversityWuhan 430072China

In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation *** most cases,it requires a larger number of data to train a robust deep learning model,which contains a lot of parameters to fit training ***,both data of user ratings and social networks are facing critical sparse problem,which makes it not easy to train a robust deep neural network *** this problem,we propose a novel correlative denoising autoencoder(CoDAE)method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for *** develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater,truster and trustee,***,on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user,we propose to utilize shared parameters to learn common information of the units that corresponding to same ***,we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE *** further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation *** experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.

关键词： social network recommender system denoising autoencoder neural network

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Applying memetic algorithm-based clustering to recommender system with high sparsity problem

引用

Journal of Central South University 2014年第9期21卷 3541-3550页

作者： MARUNG Ukrit THEERA-UMPON Nipon AUEPHANWIRIYAKUL Sansanee Department of Electrical Engineering Faculty of Engineering Chiang Mai University Department of Computer Engineering Faculty of Engineering Chiang Mai University

A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.

关键词： memetic algorithm recommender system sparsity problem cold-start problem clustering method

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Location-Aware Personalized Traveler recommender system(LAPTA)Using Collaborative Filtering KNN

引用

Computers, Materials & Continua 2021年第11期69卷 1553-1570页

作者： Mohanad Al-Ghobari Amgad Muneer Suliman Mohamed Fati School of Information Technology Asia Pacific UniversityKuala Lumpur57000Malaysia Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Department of Information systems Prince Sultan UniversityRiyadh11586Saudi Arabia

Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites,accommodation,and food according to their *** objective makes it harder for tourists to decide and plan where to go and what to *** from hiring a local guide,an option which is beyond most travelers’budgets,the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user ***,this work utilizes the prevalent recommender systems and mobile app technologies to overcome this ***,this study proposes location-aware personalized traveler assistance(LAPTA),a system which integrates user preferences and the global positioning system(GPS)to generate personalized and location-aware *** integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ***,LAPTA separates the data obtained from Google locations into name and category *** the data separation,the system fetches the keywords from the user’s input according to the user’s past research *** proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized *** system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system *** experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.

关键词： LAPTA recommender system KNN collaborative filtering users’preference mobile application location awareness

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An E-Commerce recommender system Based on Content-Based Filtering

引用

Wuhan University Journal of Natural Sciences 2006年第5期11卷 1091-1096页

作者： HE Weihong CAO Yi Department of Computer Science and Technology Hunan Institute of Technology Hengyang 421101 HunanChina School of Business Central South UniversityChangsha 410083 Hunan China

Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.

关键词： E-commerce recommender system personalized recommendation content-based filtering Vector Spatial Model(VSM)

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

An Ensemble Learning recommender system for Interactive Platforms

引用

World Journal of Engineering and Technology 2022年第2期10卷 410-421页

作者： Bernabe Batchakui Basiliyos Tilahun Betru Dieudonné Alain Biyong Lauris Djilo Tchuenkam Department of Computer Science National Advanced School of Engineering University of Yaoundé I Yaoundé Cameroon

In interactive platforms, we often want to predict which items could be more relevant for users, either based on their previous interactions with the system or their preferences. Such systems are called recommender systems. They are divided into three main groups, including content-based, collaborative and hybrid recommenders. In this paper, we focus on collaborative filtering and the improvement of the accuracy of its techniques. Then, we suggest an Ensemble Learning recommender system model made of a probabilistic model and an efficient matrix factorization method. The interactions between users and the platform are scored by explicit and implicit scores. At each user session, implicit scores are used to train a probabilistic model to compute the maximum likelihood estimator for the probability that an item will be recommended in the next session. The explicit scores are used to know the impact of the user’s vote on an item at the time of the recommendation.

关键词： Interactive Platforms recommender system Hybrid recommender Probabilistic Model Matrix Factorization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Building a better recommender system in E-commerce

引用

Journal of Chongqing University 2003年第1期2卷 68-72页

作者：黎星星 College of Computer Science Chongqing University Chongqing 400044 P.R. China

This paper presents an architecture of a hybrid recommender system in E-commerce environment. The goal of the system is to make special improvements in giving precisely personalized recommendation through some effective measures. Based on the study on the existing recommendation methods of both the conventional similarity function and the conventional feedback function, several improvement algorithms are developed to enhance the precision of recommendation, which include three improved similarity functions, four improved feedback functions, and adoption of both explicit and implicit preferences in individual user profile. Among them, issues and countermeasures of a new user, prominent preferences and long-term preferences are nicely addressed to gain better recommendation. The users preferences is so designed to be precisely captured by a user-side agent, and can make self-adjustment with explicit or implicit feedback.

关键词： recommender system users preference similarity function feedback function

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：