Web Service Recommendation System Research Articles

Multi-representation web service recommendation system based on attention mechanism

Multi-representation web service recommendation system based on attention mechanism

Service Recommendation Method based on Multi Model Fusion

<p>In recent years, the rapid development of service-oriented computing technology has increased the burden of choice for software developers when developing service-based applications. Existing Web service recommendation systems often face two challenges. First, developers are required to input keywords for service search, but due to their lack of knowledge in the relevant field, the keywords entered by the developers are usually freestyle, causing an inability to accurately locate services. Second, it is exceedingly difficult to extract services that meet the requirements due to the 99.8% sparseness of the application service interaction records. To address the above challenges, a framework for service recommendation through multi-model fusion (SRM) is proposed in this paper. Firstly, we employ graph neural network algorithms to deeply mine historical records, extract the features of applications and services, and calculate their preferences. Secondly, we use the BERT model to analyze text information and use the attention mechanism and fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. The two models mentioned above are further merged to obtain the final service recommendation list. Extensive experiments on datasets demonstrate that SRM can significantly enhance the effectiveness of recommendations in service recommendation scenarios.</p> <p> </p>

A novel light GBM-optimized long short-term memory for enhancing quality and security in web service recommendation system

A novel light GBM-optimized long short-term memory for enhancing quality and security in web service recommendation system

Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction.

Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

Qos Web Service Design Based on Collaborative Filtering

In the process of web service recommendation, the prediction accuracy of Web Service missing Quality of Service (QoS) value will have an important impact on the rationality of service recommendation. Therefore, combined with spatiotemporal similarity perception, this paper proposes a new web service QoS collaborative filtering recommendation algorithm. This paper designs the framework of web service recommendation system from the perspective of QoS collaborative prediction, and gives the definition of related parameter set. Aiming at the problem that some services in the traditional Top-k algorithm are not similar to the target services, the spatial-temporal similarity perception combined with similar weight is used to predict the missing data to improve the prediction accuracy. In this paper, the calculation process of the algorithm is given through a simple example. The effectiveness of the algorithm is verified by the experimental results.

Strategies of similarity propagation in web service recommender systems.

Recently, web service recommender systems have attracted much attention due to the popularity of Service-Oriented Computing and Cloud Computing. Memory-based collaborative filtering approaches which mainly rely on the similarity calculation are widely studied to realize the recommendation. In these research works, the similarity between two users is computed based on the QoS data of their commonly-invoked services and the similarity between two services is computed based on the common users who invoked them. However, most approaches ignore that the similarity calculation is not always accurate under a sparse data condition. To address this problem, we propose a similarity propagation method to accurately evaluate the similarities between users or services. Similarity propagation means that "if A and B are similar, and B and C are similar, then A and C will be similar to some extent". Firstly, the similarity graph of users or services is constructed according to the QoS data. Then, the similarity propagation paths between two nodes on the similarity graph are discovered. Finally, the similarity along each propagation path is measured and the indirect similarity between two users or services is evaluated by aggregating the similarities of different paths connecting them. Comprehensive experiments on real-world datasets demonstrate that our similarity propagation method can outstandingly improve the QoS prediction accuracy of memory-based collaborative filtering approaches.

An LSTM Based Time Series Forecasting Framework for Web Services Recommendation

The convergence of Social Mobility Analytics and Cloud (SMAC) technologies gives rise to an unforeseen aggrandization of the web services on the internet. The resilience and payment-based approach of the cloud makes it an obvious choice for the deployment of web services-based applications. Out of available web services, to gratify the similar functionalities, the choice of the web service based on the personalized quality of service (QoS) parameters plays an importantrole in determining the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the non-functional performance of web services correlated behavior with the invocation time. This happens because service status usually changes over time. Hence, the design of the time a ware web service recommendation system based on the personalized QoS parameters is very crucial and becomes a challenging research issue. In this study, LSTM based deep learning models were used for the prediction of these time aware QoS parameters and the results are compared with the previous approaches. The experimental results show that the LSTM based Time Series Forecasting Framework is performing better. The RMSE, MAE, and MAPE are used as an evaluation metric and their value for the prediction of Response time (RT) is found to be 0.030269, 0.02382 and 0.59773 respectively with adaptive moment estimation as the training option and is found to be 0.66988, 0.66465 and 27.9934 respectively with root mean square propagation as the training option. The RMSE, MAE, and MAPE value for the prediction of through put (TP) is found to be 0.77787, 0.4792 and 1.61 respectively with adaptive moment estimation as the training option and is found to be 0.2.7087, 1.4076 and 7.1559 respectively with root mean square propagation as the training option respectively. Thus, the experimental results show that the LSTM model of Time Series Fore casting for Web Services Recommendation Framework is performing better as compared to previous methods.

A Web service recommendation system based on QoS attribute based collaborative filtering

A Web service recommendation system based on QoS attribute based collaborative filtering

A probability distribution detection based hybrid ensemble QoS prediction approach

Recently, with the rapid increase in the number of web services, how to discover services to meet personalized requirements of users in large-scale scenarios has become a popular topic in both industry and academia. The quality of service (QoS) prediction-based web service recommendation system has been widely studied as an effective method for solving this problem. However, due to the sparsity and imbalance of the observed QoS data, existing QoS prediction approaches suffer from limited prediction accuracy and poor scalability. Moreover, existing QoS prediction approaches do not consider the probability distribution of the observed QoS data, which have remarkable impacts on the prediction performance. In this paper, we propose a novel probability distribution detection-based hybrid ensemble QoS prediction model that can dynamically integrate a set of basic prediction models to improve the prediction accuracy instead of designing complex and time-consuming models. Specifically, we first propose an enhanced CF (collaborative filtering)-based approach as the basis of the prediction model. Second, given the results of a set of other basic prediction models, in addition, we propose a distribution detection algorithm to calculate the PCWs (probability confidence weights) of those results. Finally, we combine them dynamically to obtain final results based on PCWs. Experiments based on real datasets show that our approach has higher prediction accuracy and better scalability than existing mainstream QoS prediction approaches.

Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation

Abstract The convergence of Social Mobility Analytics and Cloud (SMAC) technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of the non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turns out to be a challenging research issue. In the current work, various neural network models like Levenberg Marquardt (LM), Bayesian-Regularization (BR) and Scaled-Conjugate-Gradient (SCG) are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Pearson’s Correlation Coefficient is used as an evaluation metric and their value for the prediction of Response time is found to be 0.84985 and for throughput (TP) is found to be 0.99082 with the Levenberg Marquardt algorithm. Thus, the experimental results show that the with the Levenberg Marquardt model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time as well as throughput.

An Econometric Time Series Forecasting Framework for Web Services Recommendation

The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, various econometric models are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) value is used as an evaluation metric and their value for the prediction of Response time is found to be 1.7392e+04 and 1.7416e+04 respectively with the ARMA model. The AIC and BIC value for throughput (TP) is found to be 1.5249e+04 and 1.5334e+04 respectively with the ARIMA. The value of variance is 13.2437 and 6.8131 for RT and TP respectively which is also the lowest among other models with t-statistic greater than the p-value. Thus, the experimental results show that the ARMA and ARIMA model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time and throughput respectively.

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

SummaryWith the growing number of functionally similar services over the Internet, recommendation techniques become a natural choice to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation are static, while in the real world, the perception and popularity of Web services may continually change. Time is becoming an increasingly important factor in recommender systems since time effects influence users' preferences to a large extent. In order to help users with this problem, we propose a time‐aware Web service recommendation system. First, we use K‐means clustering method in order to exclude the less similar users, which share few common Web services with the active user at different times. Slope One algorithm is also adopted in order to deal with data sparsity problem by predicting the missing ratings over time. Then, a recommendation algorithm is presented in order to recommend the top‐rated Web services. Experiments proved the accuracy of our approach compared to five existing solutions.

A design of intelligent QoS aware web service recommendation system

In web services environment, making an appropriate service recommendation to improve the web service composition ability and decrease composition cognition is a promising approach. Web service filtering is one of the essential technique for recommending QoS based web services. Collaborative Filtering (CF) plays major role in Web service recommendation which intends to predict missing QoS values of web services. QoS prediction techniques, emphasize more on user’s personalized influence of services and service QoS factors such as response time and throughput. The proposed model merges the location preference and personalized usage influences of the requester with Privacy, Demand, Satisfaction and Retention level assessment. Neuro Fuzzy Logic (NFL) is introduced to improve classification accuracy to intelligently identify the untrustworthy Web services. Intelligent Neuro Fuzzy Collaborative filtering (INFCF) for QoS aware web service recommendation is designed to enhance the QoS in web service recommendation.

A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side

Quality of service (QoS) has been playing an increasingly important role in today’s Web service environment. Many techniques have been proposed to recommend personalized Web services to customers. However, existing methods only utilize the QoS information at the client-side and neglect the contextual characteristics of the service. Based on the fact that the quality of Web service is affected by its context feature, this paper proposes a new QoS-aware Web service recommendation system, which considers the contextual feature similarities of different services. The proposed system first extracts the contextual properties from WSDL files to cluster Web services based on their feature similarities, and then utilizes an improved matrix factorization method to recommend services to users. The proposed framework is validated on a real-world dataset consisting of over 1.5 million Web service invocation records from 5825 Web services and 339 users. The experimental results prove the efficiency and accuracy of the proposed method.

Dynamically Predicting the Quality of Service: Batch, Online, and Hybrid Algorithms

This paper studies the problem of dynamically modeling the quality of web service. The philosophy of designing practical web service recommender systems is delivered in this paper. A general system architecture for such systems continuously collects the user-service invocation records and includes both an online training module and an offline training module for quality prediction. In addition, we introduce matrix factorization-based online and offline training algorithms based on the gradient descent algorithms and demonstrate the fitness of this online/offline algorithm framework to the proposed architecture. The superiority of the proposed model is confirmed by empirical studies on a real-life quality of web service data set and comparisons with existing web service recommendation algorithms.

QOS based Approach for Web Service Recommendation Systems

QOS based Approach for Web Service Recommendation Systems

A New Improved Clustering Algorithm based Diversified Web Page Recommendation

tremendous growth of internet over the years, has given rise to the large number of web services, containing lot of information. Due to this information overload, it has become difficult to get the correct information. Web Service Recommendation system focuses on satisfying the user's potential interests. Most of the existing recommendation approaches focus only on missing QoS values only, assuming that the result contains independent web services, which might not be true. As a result redundant web services appear in the list. The existing system takes into consideration active user's QoS preferences as well as diversification of the web services list. First, the active user's usage history is mined, and then the experiences of other service users are collected through collaborative filtering approach. Scores are computed for the web service candidates by measuring their relevance with historical and potential user interest and the QoS utility. Web Service graph is constructed based on the functional similarity of the web service candidates. Finally, the diversity- aware web service ranking algorithm is applied on the web service candidates based on the scores calculated and the diversified degree derived from the web service graph. Keywordsservice recommendation, diversity, user interest,

CluCF: a clustering CF algorithm to address data sparsity problem

In QoS-based Web service recommendation, predicting Quality of Service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. Data sparsity is an important challenges for CF algorithms. Although model-based algorithms can address the data sparsity problem, those models are often time-consuming to build and update. Thus, these CF algorithms aren't fit for highly dynamic and large-scale environments, such as Web service recommendation systems. In order to overcome this drawback, this paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user (the new service) by location factor to lower the time complexity of updating clusters. Additionally, in order to improve the prediction accuracy, CluCF employs time factor. Time-aware user-service matrix Mu;s(tk, d) is introduced, and the time-aware similarity measurement and time-aware QoS prediction are employed in this paper. Since the QoS performance of Web services is highly related to invocation time due to some time-varying factors (e.g., service status, network condition), time-aware similarity measurement and time-aware QoS prediction are more trustworthy than traditional similarity measurement and QoS prediction, respectively. Since similarity measurement and QoS prediction are two key steps of neighborhood-based CF, time-aware CF will be more accurate than traditional CF. Moreover, our approach systematically combines user-based and item-based methods and employs influence weights to balance these two predicted values, automatically. To validate our algorithm, this paper conducts a series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that our approach is capable of alleviating the data sparsity problem.