A novel multi agent recommender system for user interests extraction

In this paper, a multi agent recommender system is designed and developed for user interests extraction The system consists of eight agents such as age, identity, personality, social, financial, location, and needs. The agents works with each others in a collaborative way to make recommendation to the users according to their interest. The relation between the agents and the users are controlled by a well developed protocol and pre-defined senses. The information between the users and the agents are collected in information center agent (ICA). The data collected in ICA can be used to rearrange the videos in way such that it is more relative to the user depending on his interest. This interest can be extracted from the information that the user initially provides to the system which can be then analyzed from the multi agent system to decide whether the user is interested in a video or not. This is done by creating video -important term matrix, user important term matrix and agent -feature matrix. Then, theses matrices are used by the multi-agent system to get video-Agent effective matrix for the users which leads to most ordered videos in order to be presented to the user. The proposed model was verified by intensive simulations using eight agents using JADE platform. The results show that the accuracy of the system for 50 videos that were well arranged for 40 users is 87%.