An Incremental Tensor Factorization Approach for Web Service Recommendation

Many spectral unmixing approaches ranging from geometry, algebra to statistics have been proposed, in which nonnegative matrix factorization (NMF)-based ones form an important family. The original NMF-based unmixing algorithm loses the spectral and spatial information between mixed pixels when stacking the spectral responses of the pixels into an observed matrix. Therefore, various constrained NMF methods are developed to impose spectral structure, spatial structure, and spectral-spatial joint structure into NMF to enforce the estimated endmembers and abundances preserve these structures. Compared with matrix format, the third-order tensor is more natural to represent a hyperspectral data cube as a whole, by which the intrinsic structure of hyperspectral imagery can be losslessly retained. Extended from NMF-based methods, a matrix-vector nonnegative tensor factorization (NTF) model is proposed in this paper for spectral unmixing. Different from widely used tensor factorization models, such as canonical polyadic decomposition CPD) and Tucker decomposition, the proposed method is derived from block term decomposition, which is a combination of CPD and Tucker decomposition. This leads to a more flexible frame to model various application-dependent problems. The matrix-vector NTF decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a vector (endmember) and a matrix (corresponding abundances). From a formal perspective, this tensor decomposition is consistent with linear spectral mixture model. From an informative perspective, the structures within spatial domain, within spectral domain, and cross spectral-spatial domain are retreated interdependently. Experiments demonstrate that the proposed method has outperformed several state-of-the-art NMF-based unmixing methods.

Web service recommendation is of great importance when users face a large number of functionally-equivalent candidate services. To recommend Web services that best fit a user's need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation records exist. To tackle this challenge, in recent literature, a number of QoS prediction methods are proposed, but they still demonstrate disadvantages on prediction accuracy. In this paper, we design a location-based hierarchical matrix factorization (HMF) method to perform personalized QoS prediction, whereby effective service recommendation can be made. We cluster users and services into several user-service groups based on their location information, each of which contains a small set of users and services. To better characterize the QoS data, our HMF model is trained in a hierarchical way by using the global QoS matrix as well as several location-based local QoS matrices generated from user-service clusters. Then the missing QoS values can be predicted by compactly combining the results from local matrix factorization and global matrix factorization. Comprehensive experiments are conducted on a real-world Web service QoS dataset with 1,974,675 real Web service invocation records. The experimental results show that our HMF method achieves higher prediction accuracy than the state-of-the-art methods.

Now a days, Web services are becoming the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single web service, so QoS information is an important indicator for service selection. But in reality, QoSs of some Web Services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This study first proposes a novel method for clustering similar web services using semantic approach in order to overcome the limitations of pattern-matching approach and finally proposes a cluster based approach using Slope One Collaborative filtering method to predict the QoS values for similar web services for similar users. Proposed approaches are applied on a dataset consisting of WSDL files and QoS values for various QoS parameters which are collected from the Internet and the proposed approaches shows better qualities with respect to clusters formed and the QoS values predicted.

As tensors provide a natural representation for the higher-order relations, tensor factorization techniques such as Tucker decomposition and CANDECOMP/PARAFAC decomposition have been applied to many fields. Tucker decomposition has strong capacity of expression, but the time complexity is unpractical for the large-scale real problems. On the other hand, CANDECOMP/PARAFAC decomposition is linear in the feature dimensionality, but the assumption is so strong that it abandons some important information. Besides, both of TD and CP decompose a tensor into several factor matrices. However, the factor matrices are not natural for the representation of the higher-order relations. To overcome these problems, we propose a near linear tensor factorization approach, which decompose a tensor into factor tensors in order to model the higher-order relations, without loss of important information. In addition, to reduce the time complexity and the number of the parameters, we decompose each slice of the factor tensors into two smaller matrices. We conduct experiments on both synthetic datasets and real datasets. The experimental results on the synthetic datasets validate that our model has strong capacity of expression. The results on the real datasets show that our approach outperforms the state-of-the-art tensor factorization methods.

In order to find best services to meet multi-user's QoS requirements, some multi-user Web service selection schemes were proposed. However, the unavoidable challenges in these schemes are the efficiency and effect. Most existing schemes are proposed for the single request condition without considering the overload of Web services, which cannot be directly used in this problem. Furthermore, existing methods assumed the QoS information for users are all known and accurate, and in real case, there are always many missing QoS values in history records, which increase the difficulty of the selection. In this paper, we propose a new framework for multi-user Web service selection problem. This framework first predicts the missing multi-QoS values according to the historical QoS experience from users, and then selects the global optimal solution for multi-user by our fast match approach. Comprehensive empirical studies demonstrate the utility of the proposed method.

While the usage of web services has increased explosively in recent years, very few studies examined the characteristics of web services using large-scale real data for a long period of time. In this paper, we present one such a large scale longitudinal analysis of publicly available web services of SOAP-based and RESTful types. For the period of roughly one year and from five different world-wide locations, we closely monitor the ups and downs of various basic properties of web services and their QoS values using a total of 825,132 real web services.

Users of Web and grid services often must temporarily delegate some or all of their rights to a software entity to perform actions on their behalf. The problem with the typical grid services approach (X. 509 proxy certificates) is that commercial Web services tooling fails to recognize these certificates or process them properly. The security assertion markup language (SAML) is a standardized XML-based framework for exchanging authentication, authorization and attribute information. SAML has broadening commercial support but lacks delegation capabilities. To address this shortcoming, we exploit SAML's inherent extensibility to create a delegation framework for Web and grid services that supports both direct and indirect delegation. We develop a set of verification rules for delegation tokens that rely on WS-Security X.509 signatures, but do not force any trust relationship between the delegatee and the target service. We have implemented the framework on two common Web service hosting environments: Java/Tomcat and .NET. By leveraging existing Web services standards, we make it easier for Grid practitioners to build and consume Web and grid services without resorting to grid-specific protocols.

The exponential growth of Web service makes building high-quality service-oriented applications an urgent and crucial research problem. User-side QoS evaluations of Web services are critical for selecting the optimal Web service from a set of functionally equivalent service candidates. Since QoS performance of Web services is highly related to the service status and network environments which are variable against time, service invocations are required at different instances during a long time interval for making accurate Web service QoS evaluation. However, invoking a huge number of Web services from user-side for quality evaluation purpose is time-consuming, resource-consuming, and sometimes even impractical (e.g., service invocations are charged by service providers). To address this critical challenge, this paper proposes a Web service QoS prediction framework, called WSPred, to provide time-aware personalized QoS value prediction service for different service users. WSPred requires no additional invocation of Web services. Based on the past Web service usage experience from different service users, WSPred builds feature models and employs these models to make personalized QoS prediction for different users. The extensive experimental results show the effectiveness and efficiency of WSPred. Moreover, we publicly release our real-world time-aware Web service QoS dataset for future research, which makes our experiments verifiable and reproducible.

This paper proposes a hybrid model of aspect-oriented sentiment prediction which integrates tensor factorization (TF) and sentiment utility logistic model (SULM). First, using sentiment dictionary words as seeds, the aspect or opinion words can be extended iteratively through double propagation. Accordingly, the users’ reviews could be represented as the features in user-item-aspect space, in which prediction model could be built. Various combinations of the hybrid model were proposed and evaluated on the Chinese reviews on places of interest from Trip Advisor. Experimental results show that the hybrid model can achieve better prediction performance than TF or SULM. The hybrid model also outperforms either TF or SULM while handling cold-start problem.

Web service is an emerging technology in this decade for its openness and loosely coupled interaction among systems. Due to the proliferation of web services, more number of web services is available for similar functionality. QoS deals about the non-functional information of a Web services. Furthermore, QoS plays a vital role for differentiating web services which has similar functionality. Predicting the QoS value of a web service is an important process for web service discovery and selection. Due to the different network environment includes bandwidth, capacity and delay, the QoS value of a web service is differs from user to user. Therefore, QoS prediction is imperative for a recommendation system to provide best services based on QoS values. To achieve this, an enhanced collaborative filtering approach is introduced. In this approach, the PCC (Pearson Correlation Coefficient) is calculated for identifying the similar user and items. To increase the prediction accuracy, the user’s personalized degree of similarity is calculated and the Top-k users and items are identified. The confidence value computation helps to predict the missing values in the user-item matrix. By systematic integration of user-based and item-based methods, the final QoS values of a web services is predicted. The system has been tested with the invocation of real world web services, and the efficiency of the proposed approach is compared with the existing approaches. The MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) between the proposed and existing approaches shows the accurate prediction of the proposed approach.

PurposeThis purpose of this paper is to provide details of a project to involve people with dementia in reviewing pieces of work being undertaken by Alzheimer's Society staff. Service User Review Panels (SURPs) were developed as a way to orientate participation around the requirements of service users with dementia.Design/methodology/approachSURPs took account of the specific communication and other needs highlighted in the literature on involving people with dementia. The underlying principles of SURPs concerned the quality of the relationship between the facilitator and the participants, the process of consent, the types of communication used, and the establishment of ownership. These principles were evaluated using observation of meetings, focus groups with panel members, and semi structured interviews with participating staff.FindingsDuring the pilot, SURPs fed into the work of the organisation in a number of ways including helping to set organisational priorities, and reviewing the content of tools, materials and policies. They also created increased opportunities for contact between staff and people with dementia. This paper details three examples that illustrate some of the processes involved, the issues that arose, and the learning that resulted. It concludes by outlining some of the factors of success and some of the limitations of SURPs.Originality/valueThese findings might be of use to researchers and practitioners wanting to involve people with dementia in their work or their organisation.

In recent years, multidimensional data is becoming increasingly popular in recommender systems. For example, social tagging systems encourage users to employ user-defined keywords to help manage content in a personalized way. Recommender systems built upon social tagging systems utilize social tagging data to improve recommendation accuracy. Context-aware recommender systems incorporate context information (e.g. time, location, weather, etc.) into recommendation models to make recommendations more personalized. User profiles play a crucial role in recommender systems because user profiles provide the information about users’ information needs based on which personalized recommendations can be generated. Existing user profiling techniques for collaborative filtering (CF) recommender systems based on multidimensional data mostly analyze data through splitting the multidimensional relations into lower-dimensional relations. However, this leads to the loss of multidimensionality in user-item interactions. A different type of methods for addressing multidimensional data is factorization techniques such as tensor factorization (TF), which discover holistic latent relations between all dimensions in multidimensional data. However, TF models are not constructed to analyze any particular dimension of multidimensional data. But for many application domains, certain dimensions are of particular interest such as the user dimension in recommender systems. However, user profiles are not explicitly reflected in TF models. In this paper, we propose a unified approach to profile any particular dimension in multidimensional data based on partially-decomposed Tucker models (TM). We prove that the proposed profiling approach is a compatible higher-order extension of the matrix factorization based approaches. Moreover, we prove that the proposed TM-based profiles can be updated incrementally with new data without reconstructing the entire profiles. We integrate the proposed TF-based user/item profiling approach into neighborhood-based collaborative filtering recommenders for making top-N item recommendations. Extensive experiments have been conducted using real-world social tagging datasets to demonstrate that the CF recommenders integrated with the proposed user/item profiles outperform state-of-the-art CF recommendation approaches in terms of recommendation accuracy.

Inadequate telecommunication infrastructure is one of the main reason of drawback on telecardiology implementation in developing countries. A new network selection algorithm is proposed to overcome this problem by providing adaptive heterogeneous networks. The proposed algorithm finds the most suitable wireless network according to user's health condition and service requirements. This algorithm adaptively adjusts the user bandwidth requirement by deactivating the lowest priority service level. The priority deactivation will provide sufficient bandwidth to support the higher priority service level when the best network has limited capacity. Simulation results show that the proposed algorithm improves the quality of services, cost effective and adaptive to the best networks. It outperforms conventional bandwidth based handover algorithm for telecardiology application.

This paper focuses on issues related to the selection of Web services in multi-attribute states. With the rapid development of the Internet, how to choose a quality service for users under the constraints of multi-attribute factors is a question worth thinking about. To select the optimum combination of services for the user among a large number of multi-attribute services is our goal. User requirements can be viewed as a workflow, and a complete requirements workflow contains multiple tasks. Each task consists of different services, and different tasks are used to satisfy different user requirements. How to select the optimal services from each task and combine them to form a complete service portfolio is the work we need to accomplish. In this work, we optimize the candidate service attributes and calculate the attribute weights using a weighted arithmetic mean operator, and then select the optimal service combination using an optimal ant colony algorithm. Compared with the original method in the service selection process, MDM based on OACO Algorithm can solve the multi-attribute service selection problem, while the service data after the weight calculation avoids the constraints of multiple attributes on the service selection results. The complexity of the algorithm traversal is greatly decreases by using the Cartesian product idea to group the services based on the same attribute values and then using the ant colony algorithm. For the experimental results, the multi-attribute optimization based on the optimized ant colony algorithm has significant benefits over the original algorithm in terms of time performance.

Due to recent advances in wireless technologies, wireless mesh networks (WMNs) are expected in the near future to fully handle content-rich multimedia services with various quality of services (QoS) guarantee. Unlike traditional data applications, multimedia applications require QoS support in order to satisfy the user's service requirements. Admission control has been advocated as a promising approach to tackle this challenging issue since it is a key management function for which a new flow is admitted based on the condition that the QoS requirements of all flows in the network can still be met after the new flow begins. In this paper, a Rate Adaptive Routing on Cliques Admission Control protocol (so-called RA-RCAC) for WMNs is proposed, which is able to provide feedbacks to the application layer whenever congestion occurs in the network. The RA-RCAC scheme merges the design features of two chosen benchmarking schemes, namely, an Interference-Aware Admission Control and QoS routing protocol in WMN (so-called IAAC) and a distributed admission control protocol for WMNs (so-called RCAC) to produce a protocol design that satisfies the QoS guarantee. Our proposed RA-RCAC protocol is evaluated using simulation analysis. The performance evaluation results demonstrate its superiority over the aforementioned benchmark schemes in terms of throughput, end-to-end delay, packet delivery ratio, and loss ratio performance evaluation metrics.