Service Recommendation Method Research Articles

QoS Prediction for Service Recommendation with Deep Feature Learning in Edge Computing Environment

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results.

A New Method for Web Service Recommendation Based on QoS Prediction

A New Method for Web Service Recommendation Based on QoS Prediction

Leveraging contextual information for cold‐start Web service recommendation

SummaryWeb service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques. In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold‐start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user‐service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold‐start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F‐Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance.

A New Method for Web Service Recommendation Based on QoS Prediction

As service-oriented architecture gains in popularity and grows in popularity, Web service recommendation and composition have become more important topics for research. Accurately predicting individualized QoS recommendations for recommending web services is a difficult task because of the inconsistency of the Internet and the scarcity of information regarding QoS history. Our team suggests a new framework for QoS values’ prediction and also presents two methods for clustering, User_BC and Service_BC, to support QoS prediction accuracy. Hierarchical clustering is used, based on the QoS dataset of PlanetLab1 (that) contains 200 service-user response time values, with 1,597 service values overall. In our research, we've found that our clustering-based methods beat other popular algorithms in detailed experimental comparisons and analyses.

Personalised service recommendation process based on service clustering

Personalised service recommendation is the key technology for service platforms; the demand preferences of users are the important factors for personalised recommendation. First, in order to improve accuracy and adaptability of service recommendation, services are needed to be initialised before being recommended and selected, then they are classified and clustered according to demand preferences, and service clusters are defined and demonstrated. In the sparse problem of service function matrix, historical and potential preferences are expressed as double matrices. Second, service cluster is viewed as the basic business unit, we optimise graph summarisation algorithm and construct service recommendation algorithm SCRP, helped by the experiments about variety parameters, which has more advantages than other algorithms. Third, we select fuzzy degree and difference to be the two key indicators, and use some service clusters to complete simulating and analyse algorithm performances. The results show that our service selection and recommendation method is better than others, which might effectively improve the quality of service recommendation.

Personalized Service Recommendation With Mashup Group Preference in Heterogeneous Information Network

In the service network, there exist various objects and rich relations among them. These various objects and rich relations naturally form a heterogeneous information network. Service recommendations can help users to locate their desired services. Previous service recommendation studies mainly aim at homogeneous networks or consider few kinds of relations rather than using the rich heterogeneous information. In this paper, we propose a mashup group preference-based service recommendation method in the heterogeneous information network for mashup creation. First of all, we analyze the historical invocation records between mashups and services and exploit the heterogeneous information to construct diverse meta paths with different semantic meanings. Then, we measure the similarity between the starting object and the ending object from different perspectives and integrate different similarity measures to obtain the hybrid similarity. Next, we introduce group preference to capture the rich interactions among mashups and apply a group preference-based Bayesian personalized ranking algorithm to learn the model. Finally, we recommend a list of personalized ranking services for mashup developers. A series of experiments conducted on a real-world dataset demonstrate the superiority of our proposed approach over other baseline approaches.

Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition

Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services. Although service recommendations have been recently studied, the performance of the existing ones is unsatisfactory because: 1) the current QoS predicting algorithms still experience data sparsity and cannot predict the QoS values accurately and 2) the previous approaches fail to consider the QoS variance according to the users and services' locations carefully. A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. The method is called QoSHTD that is based on location clustering and hierarchical tensor decomposition. First, the users and services of the QoSHTD cluster into several local groups based on their location and models local and global triadic tensors for the user-service-time relationship. The hierarchical tensor decomposition is then performed on the local and global triadic tensors. Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values. The comprehensive experiment shows that the proposed method achieves a high prediction accuracy and recommending quality of Web service, and can partially address data sparsity.

Using NearestGraph QoS Prediction Method for Service Recommendation in the Cloud

With the advent of the mobile network, the fusion of cloud computing and fog computing is becoming feasible to promise lower latency and short-fat connection. However, there are a lot of redundant cloud-aware services with identical functionalities but a different quality of service (QoS) in the fog cloud environment. In fact, since QoS information is stored in distributed fog servers rather than remote cloud, it is hard for individuals to make recommendation and selection with sparse QoS information. Collaborative filtering is an important method for the sparsity problems and has been widely adopted on the prediction of missing QoS values. Focusing on the fact that existing researchers often ignore the QoS fluctuation in a wide range in the fog cloud environment, a novel neighbor-based QoS prediction method is proposed for service recommendation, in which a concept and calculation method is put forward to describe the stable status of services and users with quantifiable QoS values, and a NearestGraph algorithm is further designed to recognize stable or unstable candidate along with their popularity by a nearest neighbor graph structure which can help to make missing QoS values prediction in a certain order to improve final prediction accuracy. Experimental results confirm that the proposed method is effective in predicting unknown QoS values in terms of service recommendation accuracy and efficiency.

QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications

IoT Mashup applications allow developer to compose existing Web APIs to create value-added composite Web services. The rapid growth of large-scale and complex services makes it difficult to find suitable Web APIs to build IoT Mashup applications for developers. Even if the existing service recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and multiple-dimension information of QoS between Mashup and services on recommendation accuracy. In this paper, we propose a QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. This method first uses relational topic model to characterize the relationships among Mashup, services, and their links, and mine the latent topics derived by the relationships. Second, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and services to recommend adequate relevant top-k Web APIs for target IoT Mashup creation. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, and F-measure.

Interest-Aware Service Association Rule Creation for Service Recommendation and Linking Mode Recommendation in User-Generated Service

In the area of user-generated service, service recommendation and service-linking mode recommendation can help end users select suitable service components for the creation of a new service application. However, existing service recommendation and service-linking mode recommendation methods typically focus on analyzing user-service dualistic relationship and predicting the quality of service values for service composition; the information about the latent interests of the user and the service is largely underexplored, resulting in large computational costs and weak recommendation explanations. To address these issues, we propose an interest-aware service association rule creation (SARC-IA) approach for recommending suitable service components and service linking modes to end users. The proposed solution includes two phases: 1) extracting the latent interests of the user and the service by utilizing the latent Dirichlet allocation model to train the invocation records of users; 2) forming interest subsets by collecting the users with the same interest; and 3) SARC algorithm is employed to generate the service-linking modes in the interest subsets. The performance of the proposed SARC-IA is evaluated via extensive experiments with the ProgrammableWeb data set. The experimental results show that the SARC-IA method outperforms the reference schemes in terms of its support, confidence, recall, precision, and F1-measure results.

Spatial-Temporal Aware Intelligent Service Recommendation Method Based on Distributed Tensor factorization for Big Data Applications

With the dramatic growth of public cloud offerings and heterogeneous data information, how to discover potentially valuable information from big history behavior data and design intelligent recommendation techniques has become more and more important. Due to the dynamics of cloud environment, both user behaviors and QoS (Quality of Service) performance of cloud services are sensitive to contextual information, such as time and location. However, the consideration of time and location information brings the increase in the order of rating matrix and the data sparsity problem. In view of these challenges, we propose a spatial-temporal aware intelligent service recommendation method based on distributed tensor factorization to address the above problems. First, the time and location information are introduced into the recommendation models by distinguishing time-sensitive QoS metrics and region-sensitive QoS metrics from stable QoS metrics. To deal with the sparse rating data, time slots and regions are clustered respectively. Then, a high-order tensor factorization technique is applied to mine the latent factors among users, services, time information, and location information. Moreover, to improve the scalability of our recommendation models in big data environment, a fast distributed asynchronous SGD (Stochastic Gradient Descent) mechanism is employed to get a good balance between the convergence speed and prediction accuracy. Finally, experiments based on both real-world data set and big synthetic data set are conducted to validate the effectiveness and scalability of our proposal. The experimental results show that our proposal achieves a good balance between the recommendation accuracy and scalability.

Fuzzy logic based similarity measure for multimedia contents recommendation

Collaborative filtering is one of the mainstream approaches to provide recommendations in various online environments such as Ecommerce. Although this is a popular method for service recommendation, it still suffers from sparsity issue where only a small number of rating records are available for some new items or users in the system. Consequently, the accuracy of rate prediction is often compromised. Unlike the conventional collaborative filtering methods that directly compute the similarity between users, this paper presents a fuzzy logic based approach to refine the similarity obtained using traditional approaches like Pearson correlation, Cosine, Adjusted Cosine etc. Experiments were conducted on the two popular benchmark datasets and it shows that the proposed method obtains better prediction accuracy as compare to other traditional similarity measures.

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.

Online to offline (O2O) service recommendation method based on multi-dimensional similarity measurement

With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model is called online to offline (O2O) service, which has attracted business and academic attention. The large number of O2O services on the Internet creates a scalability problem, creating massive but highly sparse matrices relating customers to items purchased. In this paper, we proposed a novel O2O service recommendation method based on multi-dimensional similarity measurements. This approach encompasses three similarity measures: collaborative similarity, preference similarity and trajectory similarity. Experimental results show that a combination of multiple similarity measures performs better than any one single similarity measure. We also find that trajectory similarity performs better than the rating-based similarity metrics (collaborative similarity and preference similarity) in sparse matrices.

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems.

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on “friend” relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems.

A Similarity-Ranking Method on Semantic Computing for Providing Information-Services in Station-Concierge System

The prevalence of smartphones and wireless broadband networks have been progressing as a new Railway infomration environment.　According to the spread of such devices and information technology, various types of information can be obtained from databases connected to the Internet. One scenario of obtaining such a wide variety of information resources is in the phase of user’s transportation. This paper proposes an information provision system, named the Station Concierge System that matches the situation and intention of passengers. The purpose of this system is to estimate the needs of passengers like station staff or hotel concierge and to provide information resources that satisfy user’s expectations dynamically. The most important module of the system is constructed based on a new information ranking method for passenger intention prediction and service recommendation. This method has three main features, which are (1) projecting a user to semantic vector space by using her current context, (2) predicting the intention of a user based on selecting a semantic vector subspace, and (3) ranking the services by a descending order of relevant scores to the user’ intention. By comparing the predicted results of our method with those of two straightforward computation methods, the experimental studies show the effectiveness and efficiency of the proposed method. Using this system, users can obtain transit information and service map that dynamically matches their context.

Personalized Trusted Service Recommendation Method based on Social Work

Personalized Trusted Service Recommendation Method based on Social Work

A time-aware hyperlink-induced topic search-based reputation evaluation method for optimal manufacturing service recommendation in distributed peer-to-peer networks

With the explosive growth of manufacturing services on a global scale, it has become an enormous challenge to recommend optimal manufacturing service in distributed manufacturing environments. Reputation evaluation is a key step to make optimal manufacturing service recommendation. However, the reputation values calculated through conventional reputation evaluation methods are static and local, and have a severe impact on the effectiveness of manufacturing service recommendation. This article presents a novel and effective reputation evaluation method for optimal manufacturing service recommendation in distributed peer-to-peer networks. The main contribution of the proposed approach is that it makes it possible to calculate the global and dynamic reputation values of enterprises by employing a time-aware hyperlink-induced topic search algorithm. This algorithm not only disseminates the reputation value of transaction enterprises, but also takes account of the temporal dimension in the process of computing reputation values. A concrete example demonstrates the feasibility of calculating reputation values of the proposed method, and an evaluation experiment reveals that both of recall rate and precision rate are improved, employing our proposed method for recommending manufacturing service.

Location-aware and personalized collaborative filtering for web service recommendation

Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

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.