Akademik Veri Yönetim Sistemi
IEEE Access, 2024 (SCI-Expanded)
Recommender systems aid users in discovering items of interest across various domains. However, these systems often suffer from popularity bias, disproportionately recommending popular items and neglecting less popular ones that may still appeal to users. We propose an efficient pre-processing technique to mitigate popularity bias in personality-aware recommender systems, which leverage users' personality traits to enhance personalization and deliver higher-quality recommendations. Our method infuses synthetic ratings into less popular items, increasing their visibility in the recommendation process. We evaluate this technique using both accuracy and beyond-accuracy metrics but recognize that these metrics alone do not fully capture a recommender system's performance. To provide a holistic evaluation, we introduce the General Performance Indicator - a comprehensive metric combining accuracy and beyond-accuracy measures. Experimental results on two publicly available real-world datasets demonstrate that while our approach may cause minor decreases in accuracy, it significantly improves the beyond-accuracy aspects of recommendation quality. These enhancements underscore the effectiveness of our method in delivering a more balanced and diverse set of recommendations, addressing the limitations of traditional accuracy-focused approaches.