RPCF Algorithm for Multi-Agent Tourism System

Nowadays, there is a vast and ever growing amount of information in the Web. The users are in need of more powerful tools to collect the vast amount of heterogeneous information. To address the problem of information overload Agents have widely been proposed as a solution to these problems. One possible system approach is to personalize the Web page and create the system which responds to the user request by potentially aggregating information from several information sources in a manner which is dependent on who the user is. Personalization is a process of gathering and storing information about users, analyzing the information and based on this analysis, delivering the information to each user at the right time. Generally, Web personalization has three categories, rule-based filtering, collaborative filtering, and content-based filtering. This paper proposes the RPCF Algorithm, which aims to identify users that have relevant interest by calculating similarity between user´s profiles. In our system, we use the rule-based personalization with collaborative filtering technique for evaluating the RPCF algorithm. Our system aims at creating a personalized agent system that automatically tailors the relevant information of the users´ request from the Web sites to produce the best recommendation. We illustrate this technique in the tourism domain