User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering

The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.

Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This 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 users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,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 parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,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 services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The 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.

QoS Evaluation for Web Service Recommendation

Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods.

Recommendation system with minimized transaction data

This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.

An Alternative-Service Recommending Algorithm Based on Semantic Similarity

With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.

A novel group-based active service protocol for migrating workflows

Based on the methods of acquaintance cache and group-based intelligent forwarding of service recommendations,a novel group-based active service(GAS) protocol for migrating workflows was proposed.This protocol did not require service requesters to discover services or resources.The semantic acquaintance knowledge representation was exploited to describe service groups and this semantic information was used to recommend service to respective clients.The experimental results show that the new protocol proposed offers better performance than other protocols in terms of first-response-time,success-scope and ratio of success-packet-number to total-packet-number.When the number of service request packet is 20,the first-response-time of GAS protocol is only 5.1 s,which is significantly lower than that of other protocols.The success-scope of GAS protocol is 49.1%,showing that GAS protocol can effectively improve the reliability of mobile transactions.And the ratio of success-packet-number to total-packet-number of GAS protocol is up to 0.080,which is obviously higher than that of other protocols.

Trust-Based Personalized Service Recommendation: A Network Perspective

Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.

Extracting Relevant Terms from Mashup Descriptions for Service Recommendation

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 ProgrammableWeb.com show that the proposed model significantly outperforms state-of-the-art methods.

Research on Service Recommendation Method Based on Cloud Model Time Series Analysis

The problem of information overload is becoming increasingly prominent,and recommendation systems are developing rapidly in various fields.How to find the most user-friendly services has become the focus.Service recommendation based on QoS is an important technology to select appropriate services for users.In this paper,a service selection method based on time series analysis of cloud model is proposed.Firstly,the noise was removed by clustering algorithm,clustering was divided,and similar user sets were obtained.Then,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 function.Finally,the recommended service was obtained by comparing TOPSIS method with ideal cloud model.The experimental results on WS-Dream dataset show that the accuracy of recommendation is improved compared with the existing recommendation algorithms.

A Time-Aware Dynamic Service Quality Prediction Approach for Services

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 composition.Our 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 slice.In 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 values.In 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.

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

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.