Novel recommendation system based on long-term composition for adaptive web services

In the era of digital web services, composition of features on the fly is inevitable. The Long-term Composed Service (LCS) entertains the composition of features to any extent, since it has an open-ended lifetime. In the proposed research work, we have intended to provide service support to run the business toward a long time commitment. Structure-based recommended system for LCSs (RS-LCSs) is proposed, where user queries and recent updation/requirements are considered for exhibiting the response through the system. In the proposed system, business has been regulated according to the time constraints. We have tested our proposed system on the standard benchmark dataset and quantitative metrics show our proposed method has performed well against the compared methods. The forecasting of business has been done through our model to address the recent queries and new requirements issues to provide an adaptive web service for the business development.     

Deep knowledge-aware framework for web service recommendation

The Journal of Supercomputing - Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of...     

A semantic web methodology for situation-aware curative food service recommendation system

Recently curative food is becoming more popular, as more people realize its benefits. Based on the theory of Chinese medicine, food itself is medicine. The curative food which is an ideal nutritious food can help to loss weight, increase immunity and is also good for curative effects in patients. In this paper, we propose a new Curative Food Service （CFS） recommendation system, the designed Situation-aware Curative Food Service Recommendation System （SCFSRS）, which provides the cooperation web-based platform for all related Mobile Users （MUs） and Curative Food Service Providers （CFSPs）, could strengthen the ability of CFS suggestion. SCFSRS is a five-tier system composed of the MUs, UDDI Registries （UDDIRs）, CFSPs, Curative Food Services Server （CFSS）, and Database Server （DS）.     

A graph-based QoS prediction approach for web service recommendation

Applied Intelligence - With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in...     

Context-aware QoS prediction for web service recommendation and selection

QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.     

Web服务个性化推荐研究综述

随着Web服务的广泛使用和互联网上服务数量的增加,如何向用户提供最佳的服务选择列表成为了新的挑战.Web服务个性化推荐实现了由被动接受用户请求向主动感知用户需求的转变.个性化的Web服务推荐方法已经成为Web服务发现和选择的有效辅助手段.Web服务的个性化推荐技术也成为了近年来服务计算领域的研究热点.对当前Web服务个性化推荐的文献进行了归类分析,总结了当前Web服务个性化推荐的技术现状、研究方法和实验的数据集,列出了未来Web服务个性化推荐研究热点和挑战.     

An intelligent recommendation system using gaze and emotion detection

Recently, recommendation system has become popular in many e-commerce websites. It helps users by suggesting products which they could buy. Existing work till now uses past feedback of user, similarity of other users’ buying pattern, or a hybrid approach in which both type of information is used. But the pitfall of these approaches is that there is a need to collect and process huge amount of data for good recommendation. This paper is aimed at developing an efficient recommendation system by incorporating user’s emotion and interest to provide good recommendations. The proposed system does not require any of aforementioned data and works without the continuous and interminable attention of the user. In this framework, we capture user’s eye-gaze and facial expression while exploring websites through inexpensive, visible light “webcam”. The eye-gaze detection method uses pupil-center extraction of both eyes and calculates the reference point through a joint probability. The facial expression uses landmark points of face and analyzes the emotion of the user. Both methods work in approximate real time and the proposed framework thus provides intelligent recommendations on-the-fly without requirement of feedback and buying patterns of users.

     

Process-context aware matchmaking for web service composition

Web service composition can help software developer design more powerful and flexible applications according to requirements of enterprise. But during compositing, how to discover suitable web services is a critical problem in design and implementing application-oriented web service technologies. The traditional keyword-based matchmaking approach is difficult to help developer to find suitable service. Current researches find that to attaching semantics to each registered service can help improve the precision of matchmaking. The improvement can help developer find more suitable service for business process. This paper proposes a novel approach of semantics-based matchmaking, which is named process-context aware matchmaking. The process-context aware matchmaking discovers the suitable service during web service composite modeling. During matchmaking, the approach utilizes not only semantics of technical process but also that of business process of a registered service, thus further improving the precision of matchmaking. We integrate the process-context aware matchmaking with business-process-driven web service composition in an integrated development environment based on Eclipse. The performance evaluation shows that performance overhead of this novel approach is acceptable.     

A bio-inspired emergent system for intelligent Web service composition and management

Some important mechanisms in neuroendocrine-immune (NEI) system are inspired to design a decentralized, evolutionary, scalable, and adaptive system for Web service composition and management. We first abstract a novel intelligent network model inspired from the NEI system. Based on this model, we then propose a method for Web service emergence by designing a bio-entity as an autonomous agent to represent Web service. As such, automatic composition and dynamic management of Web services can be achieved. Also, we build its computation platform which allows the bio-entities to cooperate over Web services and exploits capabilities of their partners. Finally, the simulation results on the platform show that Web service emergence can be achieved through self-organizing, cooperating, and compositing. The proposed method provides a novel solution for intelligent composition and management of Web services.     

基于邻接矩阵的Web服务组合

针对当前Web服务组合方法在动态性和算法时间复杂度方面存在的不足,提出一种基于邻接矩阵的服务组合方法,使用邻接矩阵表示服务间的顺序及并发关系,在构建抽象服务基础上由领域专家初步建立抽象服务的组合关系,利用Warshall算法计算传递闭包来判定服务请求是否可满足,同时构建动态服务组合流程。方法操作简单,Warshall算法时间复杂度为O(n3),在服务组合中有较好的实用性。     

Semantic web service composition testbed

A huge amount of web services are deployed on the Web, nowadays. These services can be used to fulfill online requests. Requests are getting more and more complicated over time. So, there exists a lot of frequent request that cannot be fulfilled using just one web service. For using web services, composing individual services to create the added-value composite web service to fulfill the user request is necessary in most cases. Web services can be composed manually but it is a too tedious and time consuming task. The ability of automatic web service composition to create a new composite web service is one of the key enabling features for the future for the semantic web. There are some successful methods for automatic web service composition, but the lack of standard, open, and lightweight test environment makes the comparison and evaluation of these composition methods impossible. In this paper we propose an architecture for a light weight and scalable testbed to execute, test and evaluate automatic web service composition algorithms. The architecture provides mandatory components for implementing and evaluation of automatic web service composition algorithms. Also, this architecture provides some extension mechanisms to extend its default functionalities. We have also given reference implementations for web service matchmaking and composition. Also, some scenarios for testing and evaluating the testbed are given. We have found that the performance of the composition method will dramatically decrease as the number of web services increases.     

Implicit: a multi-agent recommendation system for web search

For people with non-ordinary interests, it is hard to search for information on the Internet because search engines are impersonalized and are more focused on “average” individuals with “standard” preferences. In order to improve web search for a community of people with similar but specific interests, we propose to use the implicit knowledge contained in the search behavior of groups of users. We developed a multi-agent recommendation system called Implicit, which supports web search for groups or communities of people. In Implicit, agents observe behavior of their users to learn about the “culture” of the community with specific interests. They facilitate sharing of knowledge about relevant links within the community by means of recommendations. The agents also recommend contacts, i.e., who in the community is the right person to ask for a specific topic. Experimental evaluation shows that Implicit improves the quality of the web search in terms of precision and recall.     

Genetic programming for QoS-aware web service composition and selection

Web services, which can be described as functionality modules invoked over a network as part of a larger application are often used in software development. Instead of occasionally incorporating some of these services in an application, they can be thought of as fundamental building blocks that are combined in a process known as Web service composition. Manually creating compositions from a large number of candidate services is very time consuming, and developing techniques for achieving this objective in an automated manner becomes an active research field. One promising group of techniques encompasses evolutionary computing, which can effectively tackle the large search spaces characteristic of the composition problem. Therefore, this paper proposes the use of genetic programming for Web service composition, investigating three variations to ensure the creation of functionally correct solutions that are also optimised according to their quality of service. A variety of comparisons are carried out between these variations and two particle swarm optimisation approaches, with results showing that there is likely a trade-off between execution time and the quality of solutions when employing genetic programming and particle swarm optimisation. Even though genetic programming has a higher execution time for most datasets, the results indicate that it scales better than particle swarm optimisation.     

A cloud-based intelligent TV program recommendation system

In recent years, cloud computing technology has matured significantly, as has the development of digital TV services. This, therefore, has led to an increased demand for improved quality TV services. In this paper, cloud computing technology is used to build a program recommendation system for digital TV programs, and the Hadoop Fair Scheduler is utilized to improve processing performance. Historical data of watched TV programs are collected through an electronic program guide, and then processed using K-means clustering, term frequency/inverse document frequency and k-nearest neighbor algorithms, to obtain clusters of audience groups and to find popular TV programs for each cluster. The proposed system can process massive amounts of user data in real-time, and can easily be scaled up.     

An evidence-based scheme for web service selection

Existing studies on the web service selection problem focus mainly on the functional QoS properties of the service rather than the consumer satisfaction and trust aspects. While a good QoS enhances the reputation of a service, different consumers invariably hold differing views of the service contents. Some service reputation approaches primarily consider the consumer’s prior experience of the service via opinion feedback system, may neglect the effect of social trust transition in the recommendations of others. As a result, the problem of reaching consensus on the level of consumer trust regarding service becomes one of key issues in service selection. This study proposes a trust-based service selection model to estimate the degree of consumer trust in a particular service based on the consumers’ direct experience and indirect recommendation of the service. In the proposed approach, the degree of consumer trust is correctly estimated by extending Dempster–Shafer evidence reasoning theory to the reputation computation using consumers’ direct experience and incorporating Jøsang’s belief model for solving the trust transition problem in the indirect recommendation of the service. The proposed model effectively enables deception detection by means of existing bodies of evidence, and therefore excludes the fraudulent evidence of malicious evaluators from the selection process. In addition, a quality index is proposed to help third party (TTP) examine the body of evidence and make the outranking result more reliable. Importantly, the quality index is based not only on the confidence degree of the evidence, but also on the support degree, and therefore discovers the effects of intentional negative assessments. The validity of the proposed approach is demonstrated numerically by means of two service selection examples.     

Dynamic web service composition based on OWL-S

Composing existing web services for enterprise applications may enable higher level of reuse. However the composition processes are mostly static and lack of support for runtime redesign. In this paper, we describe our approach to the extension of the OWL-S ontology framework for dynamic web service composition. We raise the level of abstraction and propose an abstract service layer so that web services can be com- posed at the abstract service level instead of the concrete level. Each abstract service is attached with an instance pool including all instances of the abstract service to facilitate fail-over and dynamic compositions.