Cross Domain Framework for Implementing Recommendation Systems Based on Context Based Implicit Negative Feedback

The last decade met a remarkable proliferation of P2P networks, PDMS, semantic web, communitarian websites, electronic stores, etc. resulting in an overload of available information. One of the solutions to this information overload problem is using efficient tools such as the recommender system which is a personalization system that helps users to find items of interest based on their preferences. Several such recommendation engines do exist under different domains. However these recommendation systems are not very effective due to several issues like lack of data, changing data, changing user preferences, and unpredictable items. This paper proposes a novel model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. This model is based on studying implicit negative feedback from users in cross domain collaborative environment to identify user preferences effectively. The authors have also identified a list of parameters for this study.