Analyzing effects of random perturbation approaches on the popularity bias issue of recommendation algorithms and developing novel fake rating injection-based solutions

This thesis explores the advancement of recommender systems, with a focus on addressing popularity bias through privacy-preserved collaborative filtering techniques. Recommender systems are pivotal in filtering vast information spaces, guiding users towards items of interest. However, these systems often suffer from popularity bias, where popular items are disproportionately recommended, overshadowing less known items. This work introduces two novel approaches: a robust method for privacy-preserved collaborative filtering and the EquiRate algorithm for mitigating popularity bias. The privacy-preserved collaborative filtering technique employs randomized perturbation and obfuscation to safeguard user privacy while maintaining recommendation quality. This method not only enhances user trust by protecting sensitive information but also contributes to the accuracy and reliability of the recommendations. On the other hand, the EquiRate method specifically addresses the challenge of popularity bias. By integrating the FusionIndex metric, which assesses both recommendation accuracy and diversity, EquiRate efficiently balances the representation of popular and niche items, promoting a more diverse and equitable item exposure. Experimental evaluations on benchmark datasets reveal that these methods significantly improve recommendation diversity without compromising accuracy. The robust privacy-preserved collaborative filtering demonstrates resilience against various privacy attacks, ensuring effective recommendation under stringent privacy constraints. Meanwhile, EquiRate outperforms existing popularity-debiasing methods across multiple datasets, as evidenced by its superior FusionIndex scores. These outcomes highlight the potential of integrating privacy preservation and bias mitigation strategies to enhance the overall effectiveness of recommender systems. In conclusion, this thesis contributes to the recommender system literature by presenting innovative solutions for two pressing issues: privacy preservation and popularity bias. The proposed methods not only advance the state-of-the-art in collaborative filtering but also pave the way for creating more balanced, accurate, and privacy-conscious recommendation platforms.