A New Recommender System based on Cooperative Co-evolution Algorithm

Expansion of global networks and storing the extensive amount of information in various websites, create a serious needs for filtering the irrelevant information in a personalized way. Collaborative filtering or recommender system is a filtering technique that allows incorporation of the profiles which can be implicitly learned from previous activities [1]. We have proposed the CoCo-CF1 as an effective method suitable for collaborating filtering running in a Jini-grid computing2 platform and operational in a distributed environment. The CoCo-CF generates representative records from stored preferences and seeks for the answer with the best fitness in the recommender system. We have considered the user satisfaction rate, feasibility of available results, user familiarity and average response time as the evaluation factors. Also we have focused on mean absolute deviation, mean square error and ranked evaluation as the performance evaluation parameters. The obtained results confirm that the CoCo-CF is a successful method for collaborative filtering.