Authors :

Dr Mohamed Doheir

Address :

Senior lecturer, Faculty of Technology Management & Technopreneurship, Universiti Teknikal Malaysia

Abstract :

Recommendation systems have become an integral part of our daily lives, providing personalized information and content tailored to individual preferences. Collaborative filtering (CF) is a widely adopted technique in recommendation systems, excelling at delivering high-quality recommendations by identifying users with similar preferences based on their past interactions and history. The Covering Rough Set (CRS) model introduces a unique approach where relevant items from each user's neighborhood collectively form a common covering. These common coverings, in turn, construct a covering set for an active user within a specific sphere. Employing covering reduction techniques helps eliminate redundant common coverings, optimizing the recommendation process. In this paper, we present a novel approach, the "Covering Rough Set-Based Collaborative Filtering Technique" (CRS-CF). This technique empowers users by learning weights on various features and harnesses rough set theory for the efficient representation of user characteristics. CRS-CF offers a personalized and robust recommendation mechanism by combining the strengths of CF and covering-based rough sets. Our study demonstrates the effectiveness of the proposed CRS-CF approach through comprehensive experimental results. We evaluate its performance using various criteria and a Social Tagging dataset. The results underscore the superiority of our approach in providing accurate and tailored recommendations, reaffirming the potential of the CRS-CF model in enhancing recommendation systems and furthering the field of personalized content delivery.

Keywords :

Covering Rough Set, Collaborative Filtering, Social Tagging Systems, Recommender Systems, Root Mean Squared Error, and Mean Absolute Error.