Situation-Aware Rating Prediction Using Fuzzy Rules

Context-Aware Recommendation Systems (CARS) extend traditional recommendation systems by adapting their output to users’ specific contextual situations. Rating prediction in CARS has been tackled by researchers attempting to recommend appropriate items to users. However, in rating prediction, three thriving challenges are still to tackle: (i) the weight of each context dimension; (ii) the correlation between context dimensions; and (iii) situation inference. A major shortcoming of the classical methods is that there is no defined way to study dependencies and interactions existing among context dimensions. Context-aware algorithms made a strong assumption that context dimensions weights are the same or initialized with random values. To address these issues, we propose a novel approach for weighting context dimensions, studying the correlation between them to infer the current situation then predict the rating based on the inferred situation. Through detailed experimental evaluation we demonstrate that the proposed approach is helpful to improve the prediction accuracy.