Exploiting Relevant Context with Soft-Rough Sets in Context-Aware Video Recommender Systems

Abstract

The adoption of the contextual information in recommender systems is fairly recent. Context-Aware Recommender Systems (CARS) provide better-personalized recommendations by utilizing contextual features in comparison to the classical two-dimensional recommendation process. In addition, selection of the specific recommender algorithm has a direct impact on the performance of CARS. In the context of video recommender systems, roughness exist in deciding which context is better to choose when a user had watched same video in more than one contextual scenarios. Therefore, selection of appropriate context and recommender algorithm for developing CARS is identified as potential research challenge. This problem may be investigated by considering a formal approximation on contextual attributes. In this article, we investigate the applicability of using soft-rough sets for formulating such an approximation on a sample real world scenario. The main objective of this study is demonstrating the applicability of soft-rough sets on CARS for removing roughness and improving contextual information selection process. The experimental results revealed that only 27% of the rules were identified by applying Rough sets on context-aware video recommender systems (CAVRS). These results can be further improved using Soft-rough sets by representing the given scenario in Boolean-valued information systems. Additionally, a few recommendations for future work on CARS are proposed.