Proposed RCFS-CARS Framework with Noise Detection and Correction

ABSTRACT

Context-Aware Recommender Systems generate more relevant recommendations by adapting them to the specific contextual situation and have become one of the most active research areas in the recommender systems. However, there remains a key issue as how contextual information can be used to create intelligent and useful recommender systems. Use of too many context variables increases dimensionality that leads to loss of accuracy while few context variables fail to bring contextual effects in recommendations. To assist the development and use of context-aware capabilities, we propose a framework, RCFS-CARS that uses influential contexts, make their sets with appropriate relaxation to be applied on different parts of the algorithm. We also propose a strategy to detect the noisy ratings in the datasets and fix them to refine the recommendation results. The experimental results on two datasets reveal that the noise detection and correction process in RCFS-CARS method is much superior and effective in context-aware recommendation scenarios.