OCRG: A proposed recommender for mitigating new user problem

In this paper, we propose an Online Cold Recommendation Generator (OCGR) to find recommendations for new users. It is based on their demographic attributes taking into account positive and negative ratings of other users. On the bases of these ratings, the proposed generator finds attraction, repulsion and balanced inclination of new users towards the existing items in the knowledge base. The results show that recommendations which are generated by using balanced inclination approach are less prone to rejection as compared to those recommendations which are generated by using only the attraction of new users towards existing items.