RECOMMENDER SYSTEM BASED ON AFFECTIVE FEEDBACK INCORPORATING HYBRID OPTIMIZATION ALGORITHM

Abstract

Websites have become more and more dynamic but they still lack intelligence. Although websites are able to mould themselves according to users’ preferences and mouse clicks yet they cannot predict the content user might like, intelligently. The amount of data collected online by web-sites is increasing and therefore the demand for unique content by users is increasing every day. This need for self-organizing and transforming websites, to suit every customer’s requirements has become a challenging problem. This work concentrates on solving the problem of creating relevant content for each user. To achieve the required flexibility we propose using mouse movements as a way of capturing the points of interest or the points where user had the most focus by capturing his mouse locations, since mouse pointer usually follows the eye trails for reaching the point of interest on website. These mouse movements can be used for studying the patterns of user behaviour when exposed to a similar reference web-page by using a pattern analysis technique like Back-propagation neural networks, which enables using soft computing to recursively map users into groups/clusters with similar interests. These clusters can be related to the historical observations as documented in the well-known and referenced MovieLens database, dynamically, as new users provide rating to movies on the system. The correlation between these two systems can be attained by using Teacher Learning Optimization framework. The proposed algorithm therefore produces very effective results even on a cold start and produces linear precision.