A design approach for Contextualized collaborative recommender system by nurturing the user interactive feedback

The study nurtures the construction of user interaction in collaborative filtering system during recommendation time and thereby, seek for the contextual information to form contextualized collaborative recommendation. Firstly, contextualized token recommendation have been based on the memory based collaborative filtering techniques where recommendation for a given user is computed based on the weighted neighborhood technique for prediction which ultimately stores the long-term interest of the user and serves as a user context. Furthermore, our system let the users interact with the recommendation, which change the weight of the prediction and form the user short-term preference. We argue that user feedback upon recommendation given by a system, help a system to model user context during such feedback and in preliminary settings provide experimental evidence to support the claim