Context-Based Satisfaction Modelling for Personalized Recommendations

Approaches for the personalized recommendations focus mainly on the user's activity over various portals. User's preferences are not dependent on the long term users' history only, but actual user's situation plays crucial role in the user's preferences adjustment and formation. An item liked by the user in some context, can be disliked by the same user in the other context. For the considering this users' variability we propose a novel approach for the user's satisfaction modelling based on incorporating the user's context into the rating prediction and consideration of previous users' rating history. Our novel approach reflects natural characteristics of user's context, when the various context's settings can influence another context. Proposed approach brings statistically significant improvement in the rating prediction process, thus it can increase user satisfaction during one-session recommendation.