In this paper a new method towards automatic personalized recommendation based on the behavior of a single user in accordance with all other users in web-based information systems is introduced. The proposal applies a modified version of the well-known Apriori data mining algorithm to the log files of a web site (primarily, an e-commerce or an e-learning site) to help the users to the selection of the best user-tailored links. The paper mainly analyzes the process of discovering association rules in this kind of big repositories and of transforming them into user-adapted recommendations by the two-step modified Apriori technique, which may be described as follows. A first pass of the modified Apriori algorithm verifies the existence of association rules in order to obtain a new repository of transactions that reflect the observed rules. A second pass of the proposed Apriori mechanism aims in discovering the rules that are really inter-associated. This way the behavior of a user is not determined by ''what he does'' but by ''how he does''. Furthermore, an efficient implementation has been performed to obtain results in real-time. As soon as a user closes his session in the web system, all data are recalculated to take the recent interaction into account for the next recommendations. Early results have shown that it is possible to run this model in web sites of medium size.
Abstract:Agent technology has been suggested by experts to be a promising approach to fully extend Intelligent Tutoring Systems (ITS). By using intelligent agents in an ITS architecture it is possible to obtain an individual tutoring system adaptive to the needs and characteristics of every student. The general architecture of the ITS proposed is formed by the three components that characterize an ITS -the Student Model, the Domain Model, and the Education Model. In the Student Model the knowledge that the system has about the student (profile and interaction with the system) is represented. In the Domain Model the knowledge about the contents to be taught is stored. Precisely, in this model four autonomous agents -the Preferences Agent, the Accounting Agent, the Exercises Agent and the Tests Agent -have been defined. Lastly, the Education Model provides the functionality that the teacher needs. Across this module, the teacher changes his preferences, gives reinforcement to the students, obtains statistics and consults the matter.
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