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Extracting Relevant Terms from Mashup Descriptions for service recommendation

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Tsinghua Science and Technology 2017年第3期22卷 293-302页

作者： Yang Zhong Yushun Fan Department of Automation Tsinghua UniversityBeijing 100084China

Due to the exploding growth in the number of web services, mashup has emerged as a service composition technique to reuse existing services and create new applications with the least amount of effort. service recommendation is essential to facilitate mashup developers locating desired component services among a large collection of candidates. However, the majority of existing methods utilize service profiles for content matching, not mashup descriptions. This makes them suffer from vocabulary gap and cold-start problem when recommending components for new mashups. In this paper, we propose a two-step approach to generate high-quality service representation from mashup descriptions. The first step employs a linear discriminant function to assign each term with a component service such that a coarse-grained service representation can be derived. In the second step, a novel probabilistic topic model is proposed to extract relevant terms from coarse-grained service representation. Finally, a score function is designed based on the final high-quality representation to determine recommendations. Experiments on a data set from *** show that the proposed model significantly outperforms state-of-the-art methods.

关键词： service recommendation topic model mashup descriptions linear discriminant function

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Research on service recommendation Method Based on Cloud Model Time Series Analysis

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国际计算机前沿大会会议论文集 2020年第1期 655-665页

作者： Zhiwu Zheng Jing Yao Hua Zhang School of Software Hunan University of Information TechnologyChangshaChina School of Computer Central South UniversityChangshaChina

The problem of information overload is becoming increasingly prominent,and recommendation systems are developing rapidly in various *** to find the most user-friendly services has become the *** recommendation based on QoS is an important technology to select appropriate services for *** this paper,a service selection method based on time series analysis of cloud model is ***,the noise was removed by clustering algorithm,clustering was divided,and similar user sets were ***,the cloud model was established by using similar user history data in different periods,and the comprehensive cloud model was obtained by combining time decay ***,the recommended service was obtained by comparing TOPSIS method with ideal cloud *** experimental results on WS-Dream dataset show that the accuracy of recommendation is improved compared with the existing recommendation algorithms.

关键词： Cloud model Time series Clustering algorithm service recommendation

Content Feature Extraction-based Hybrid recommendation for Mobile Application services

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Computers, Materials & Continua 2022年第6期71卷 6201-6217页

作者： Chao Ma YinggangSun Zhenguo Yang Hai Huang Dongyang Zhan Jiaxing Qu Harbin University of Science and Technology Harbin150040China Harbin Institute of Technology Harbin150001China The Ohio State University Columbus43202USA Heilongjiang Province Cyberspace Research Center Harbin150001China

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of ***,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to *** is the typical problem of cold start in the field of collaborative filtering *** problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy *** solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this ***,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency *** improves the accuracy of describing service attributes and the rationality of the method of calculating service ***,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application ***,the recommendation process is completed by combining the item-based collaborative filtering recommendation *** experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.

关键词： service recommendation cold start feature extraction natural language processing word mover’s distance

Recommend trustworthy services using interval numbers of four parameters via cloud model for potential users

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Frontiers of Computer Science 2015年第6期9卷 887-903页

作者： Hua MA Zhigang HU School of Software Central South University Changsha 410075 China School of Information Science and Engineering Hunan International Economics University Changsha 410205 China

How to discover the trustworthy services is a challenge for potential users because of the deficiency of us- age experiences and the information overload of QoE （qual- ity of experience） evaluations from consumers. Aiming to the limitations of traditional interval numbers in measuring the trustworthiness of service, this paper proposed a novel ser- vice recommendation approach using the interval numbers of four parameters （INF） for potential users. In this approach, a trustworthiness cloud model was established to identify the eigenvalue of INF via backward cloud generator, and a new formula of INF possibility degree based on geometrical anal- ysis was presented to ensure the high calculation precision. In order to select the highly valuable QoE evaluations, the similarity of client-side feature between potential user and consumers was calculated, and the multi-attributes trustwor- thiness values were aggregated into INF by the fuzzy ana- lytic hierarchy process method. On the basis of ranking INF, the sort values of trustworthiness of candidate services were obtained, and the trustworthy services were chosen to recommend to potential user. The experiments based on a real-world dataset showed that it can improve the recommendation accuracy of trustworthy services compared to other approaches, which contributes to solving cold start and information overload problem in service recommendation.

关键词： service recommendation trustworthiness interval numbers of four parameters cloud model potential users

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A Time-Aware Dynamic service Quality Prediction Approach for services

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Tsinghua Science and Technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of service(QoS)prediction time-aware service recommendation

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