Personalized Web Services Selection

Personalized Web Services Selection

Tarek Helmy (King Fahd University of Petroleum and Minerals, Saudi Arabia) and Ahmed Al-Nazer (King Fahd University of Petroleum and Minerals, Saudi Arabia)
DOI: 10.4018/978-1-61520-973-6.ch007
OnDemand PDF Download:
No Current Special Offers


Web services have gained an increasing popularity over the Internet. Because of today’s wide variety of services offered to perform a specific task. The task of finding selected Web services to perform a specific task becomes very hard, and it is essential that users are supported in the eventual selection of appropriate services. Web services are a great application area for agent techniques and a great substrate for developing serious autonomous agent-based systems to support a personalized Web services selection. In this chapter, we present a Collaborative Autonomous Interface Agent (CAIA) that collaborates with the Internet search engines and supports the user in finding exactly the Web services consistent with his/her needs. CAIA system has been designed, fully implemented and tested. As a case study, the testing results show a big improvement in the relevancy of the retrieved results and of the user’s satisfaction by using CAIA+Google compared to using Google only.
Chapter Preview

Personalization; carry out retrieval for each user incorporating his/her interests; of Web searching process has long been a topic of study, (Eirinaki, Vazirgiannis, 2003), (Lee, Tsai, 2003), (Sugiyama et. al., 2004), (Tijerino et. al., 2007), (Somlo, Howe, 2003), (Joana, Gauch, 2004). Customized intelligent search agents (Helmy, 2006), (Helmy et. al., 2003), (Helmy, Al-Nazer, 2007) may allow institutions to offer tools focused internally on their own collections. Offered perhaps through a web portal, these search tools will return reliable, accessible results for the campus community. As the tools become more sophisticated they will be able to search different collections of a variety of materials, regardless of format or of where the materials may be housed. The following examples show how intelligent searching is being applied in various settings.

  • Arts & Design. Using new search tools like IBM’s Query by Image Content (QBIC), users sift through the online databases of thousands of images, specifying content-based parameters like texture, shape and color that search the visual properties of images without using text descriptors.

  • Medicine. Medicine students use a special search tool with integrated thesaurus to locate references tagged with a variety of related keywords, producing an extensive list of resources around a single topic.

  • Science. Using intelligent search agents and a technology like RSS, a biologist creates a custom Web page that automatically finds and posts new research abstracts in his/her field as they are published.

  • Theater. A costume designer collects images and descriptions of period clothing, easily locating source documents related to a particular period with a single search, whether the documents reside on his/her own system, the university’s digital archives, or elsewhere on the Internet.

Regarding our approach to personalized Web service discovery and selection, we briefly survey ongoing Web service standardization activities and relate them to other work concerned with interface agents for personalization of Web portals search as follows:

Complete Chapter List

Search this Book: