Personalized Search Agents Using Data Mining and Granular Fuzzy Techniques

Abstract

In a traditional library system, a search result will be exactly the same if different users with different preferences use the same search criteria. It is obvious that the traditional library system cannot provide high QoS (Quality of Service) for different users. To solve this problem, a personalized library search agent technique is proposed based on data mining technology. By mining the training data sets, the attributes that are related to borrowing tendency of users are analyzed, and then users are divided into different groups. The SLIQ (Supervised Learning In Quest) algorithm is used to mine data in this Web-based personalized library search agent system successfully. Simulations have shown the personalized library search agent can generate personalized search results based on users’ preferences and usage. Therefore, a user can use the personalized library search agent to get quick result. The fuzzy Web search agent and the granular Web search agent are proposed to deal with uncertainty and complexity of huge amounts of Web data. In general, Computational Web Intelligence (CWI) can be used in the personalized search agent to improve QoS for Web users.