Personalized Web Services Research Articles

Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering

With the increasing amount of web services on the Internet, personalized web service selection and recommendation are becoming more and more important. In this paper, we present a new similarity measure for web service similarity computation and propose a novel collaborative filtering approach, called normal recovery collaborative filtering, for personalized web service recommendation. To evaluate the web service recommendation performance of our approach, we conduct large-scale real-world experiments, involving 5,825 real-world web services in 73 countries and 339 service users in 30 countries. To the best of our knowledge, our experiment is the largest scale experiment in the field of service computing, improving over the previous record by a factor of 100. The experimental results show that our approach achieves better accuracy than other competing approaches.

Personalized Web services selection method based on collaborative filtering

Personalized Web services selection method based on collaborative filtering

Personalized Web Service Recommendation Based on User Interest

In this paper the web service recommendation is introduced firstly. Then an improved personalized web service recommendation algorithm based on user interest is proposed. Furthermore, the computing methods of user evaluation similarity and reliability degree of web service recommendation are described too. Finally, The results of simulation experiments show that the improved personalized web service recommendation algorithm based on user interest has much higher capacity of recommendation than the recommendation based on user content and is suitable for web service recommendation.

Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

With the increasing presence and adoption of web services on the World Wide Web, the demand of efficient web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming web service invocations, this paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantages of the past web service usage experiences of service users. We first apply the concept of user-collaboration for the web service QoS information sharing. Then, based on the collected QoS data, a neighborhood-integrated approach is designed for personalized web service QoS value prediction. To validate our approach, large-scale real-world experiments are conducted, which include 1,974,675 web service invocations from 339 service users on 5,825 real-world web services. The comprehensive experimental studies show that our proposed approach achieves higher prediction accuracy than other approaches. The public release of our web service QoS data set provides valuable real-world data for future research.

Efficient personalization and Recommendation Methods for Web Mining

With the fast development of World Wide Web, Web-based applications and services should allow user to get the right personalized information quickly and effectively. Collaborative Filtering acts a very important role in web service personalization and Recommender System. Most of the research efforts in web personalization correspond to the evolution of extensive research in web usage mining, i.e. the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. Moreover, the structural properties of the web site are often disregarded. In this thesis, we propose novel techniques that use the content semantics and the structural properties of a web site in order to improve the effectiveness of web personalization. In the first part of our work we present standing for Semantic Web Personalization, a personalization system that integrates usage data with content semantics, expressed in ontology terms, in order to compute semantically enhanced navigational patterns and effectively generate useful recommendations. To the best of our knowledge, our proposed technique is the only semantic web personalization system that may be used by non-semantic web sites. In the second part of our work, we present a novel approach for enhancing the quality of recommendations based on the underlying structure of a web site. We introduce UPR (Usage-based PageRank), a PageRank-style algorithm that relies on the recorded usage data and link analysis techniques. Our experimental results show more efficient than existing works. Key words: Web personalization, Semantic web and Recommender systems.

Interacting with Commercial Web Services in Novel Ways: Google Maps as an Example of Personalizing Web Services

Besides providing a generic interface for all users, many Web services also provide tools that may be used to both customize the access of the Web services and allow interfacing with programming on the client machine. This article presents three examples of accessing Google Maps to meet a customized need. These examples provide an introduction to customizing Google Maps. At the same time, the techniques used provide an introduction to customizing data, which is a step in developing mashups.

COWES: Web user clustering based on evolutionary web sessions

As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services and motivates long term research interests in the web community. Most of the existing approaches cluster web users based on the snapshots of web usage data, although web usage data are evolutionary in the nature. Consequently, the usefulness of the knowledge discovered by existing web user clustering approaches might be limited. In this paper, we address this problem by clustering web users based on the evolution of web usage data. Given a set of web users and their associated historical web usage data, we study how their usage data change over time and mine evolutionary patterns from each user’s usage history. The discovered patterns capture the characteristics of changes to a web user’s information needs. We can then cluster web users by analyzing common and similar evolutionary patterns shared by users. Web user clusters generated in this way provide novel and useful knowledge for various personalized web applications, including web advertisement and web caching.

Combining subjective and objective QoS factors for personalized web service selection

Abstract Web service is an emerging Internet technology to dynamically describe, discover and communicate. According to the Web Service Architecture (WSA) published by W3C, users can find services through the repository Universal Description, Discovery and Integration (UDDI). However, the UDDI may find many web services with similar functions without considering the nonfunctional quality of service (QoS) information. Under such circumstances, users may have difficulty deciding which service is suitable. This paper proposes a fuzzy-based UDDI with QoS support. Unlike many similar researches, the proposed method tries to consider not only the objective factors described by service providers but also the subjective information with trustability evaluations from users who use those services. Genetic algorithm (GA) is adapted to learn user preferences, and fuzzy logic is applied for making decisions. With a fuzzy query interface to input subjective and objective factors, users can determine the most suitable web service for personal use.

The Attraction of Internet Personalization to Web Users

Numerous companies are taking advantage of interactive technology to personalize their interactions with users. However, in contrast to the proliferation of personalized web services worldwide, Information Systems (IS) research on users' attitudes towards personalized service is minimal. It remains an empirical question as to whether online firms can use personalization as a marketing strategy to attract new users. Our research examines how users' web experience and their perceptions towards personalization influence their attitudes towards switching to a personalized website. A survey study with 238 subjects was conducted. The findings provide empirical evidence showing that if users have involved interactions with their current site, then personalized services are not attractive enough to motivate them to migrate from one site to another. Users want useful personalized services, but at the same time, they are concerned with the manner in which firms use their data for personalization. Practical implicat...

Through Personalized Web Service Composition Specification: From BPEL to C-BPEL

Over the last few years, Web services technologies offered a new and successful way for interoperability among web applications. A Web service is a software system designed in a way that other software components and humans can discover and invoke to satisfy different needs. The vision of WS as a software component allows to combine several WS, providing a global value-added WS, called composite WS.Although there are several researches in web services composition, more effort should be focused on its personalization, particularly regarding how well the composition results correspond to what the user really wants. Accordingly, we present in this paper an approach that may contribute to the personalization of web services composition specification. Our approach is a context-based proposal that makes services composition specification more efficient by taking into account both user context, needs, and preferences and web services context, and by integrating them to the composition process. In addition, to permit the reuse of specifications, we enhance BPEL by developing a specification language based on context to be used in composition that we called C-BPEL.

An Empirical Examination of the Effects of Web Personalization at Different Stages of Decision Making

Personalization agents are incorporated in many Web sites to tailor content and interfaces for individual users. In contrast to the proliferation of personalized Web services worldwide, empirical research on the effects of Web personalization is scant. How does exposure to personalized offers affect subsequent product consideration and choice outcome? Drawing on literature in human-computer interaction (HCI) and user behavior, this research examines the effect of three major elements of Web personalization strategies on users' information processing through different decision-making stages: personalized content quality, feature overlapping among alternatives, and personalized message framing. These elements can be manipulated by a firm during implemention of its personalization strategy. A study using a personalized ringtone download Web site was conducted. The findings provide empirical evidence of the effects of Web personalization. In particular, when users are forming their consideration sets, the agents can play a role in helping users discover new products or generate demand for unfamiliar products. Once a decision has been made, however, the personalization agent's persuasive effects diminish. These results establish that the role of personalization agents changes at different stages of users' decision-making process.

Views in composite Web services

The authors present a view-based approach for tracking personalized Web services. To guarantee proper handling of user preferences during Web service execution, a view can zoom into the specification that composes the Web services. As time advances, the location changes, or constraints on the environment are satisfied, the deployment of a view over a specification similarly progresses, mirroring the context's dynamic nature.

Dynamic and Personalized Web Services Composition in E-Business

Building composite Web services can save significant time and cost for developing new applications and enhancing the interoperability and collaboration among E-business partners. This article proposes a framework for dynamic and personalized composition of Web services using an approach that integrates not only functional attributes of Web services, but also nonfunctional attributes such as service requirements, quality of service, and the preferences and constraints of individual service consumers. A prototype system based on the proposed framework and some implementation details are also presented.

클릭스트림 분석을 위한 웹 서버 시스템의 설계 및 구현

인터넷을 통한 비즈니스에 수익 모델에 대한 관심이 높아지면서 방문자별로 개인화된 서비스를 제공하려는 노력이 커지고 있다. 개인화(Personalization)란 고객 한 명을 대상으로 하여 그 고객 한 사람을 위한 정보나 제품을 제공해주는 작업을 말한다. 개인화 서비스를 위해서 전 처리과정인 사용자 프로파일 생성과정이 필요하며, 적극적인 개인화 서비스를 제공하기 위해서는 충분한 고객 데이터가 필요하다. 본 논문에서는 웹사이트 상에서 사용자 행위 패턴을 파악할 수 있는 클릭스트림 정보를 모듈화 하였으며, 이를 이용하여 확장된 웹 로그 시스템을 구현하였다. 클릭스트림 정보를 웹 로그정보에 포함시켜 사용자의 행위 패턴을 파악할 수 있도록 웹 서버 시스템을 설계하고 구현하였다. 그리고 이 웹 서버는 웹사이트로부터 얻은 클릭스트림 정보를 분류하고 저장하여 관리자가 쉽게 분석할 수 있다. 이때 데이터베이스 저장 기술로 OLE DB Provider상에서 수행되는 ADO(ActiveX Data Object)기술을 사용함으로써 확장된 웹 로그 처리 시스템을 설계하였다. 확장된 웹 로그 DB를 패턴분석, 군집분석 등의 마이닝(Mining) 기법을 통하여 맞춤서비스에 대한 사용자 프로파일을 구축할 수 있다. Clickstream is the information which demonstrate users' path through web sites. Analysis of clickstream shows how web sites are navigated and used by users. Clickstream of online web sites contains effective information of web marketing and to offers usefully personalized services to users, and helps us understand how users find web sites, what products they see, and what products they purchase. In this paper, we present an extended web log system that add to module of collection of clickstream to understand users' behavior patterns In web sites. This system offers the users clickstream information to database which can then analyze it with ease. Using ADO technology in store of database constructs extended web log server system. The process of making clickstreaming into database can facilitate analysis of various user patterns and generates aggregate profiles to offer personalized web service. In particular, our results indicate that by using the users' clickstream. We can achieve effective personalization of web sites.