Hybrid Recommender Systems: Process, Challenges, Approaches and Metrics - Reka, K; Ravi, T.N (International Journal of Computer Sciences and Engineering, 2025)
Recommender systems plays a significant role, by providing personalized information to users over the internet. With the evolution of the internet, the recommender systems too have evolved from being based on simple demographics, user and item information, into complex hybrid models capable of providing an effective real-time recommendation on a per-user basis. This works provides an overview of traditional recommender system approaches, their taxonomy and discusses the various hybridization techniques used for creating complex models that provide hyper-personalized recommendations. A detailed discussion of the research challenges and how they impact the performance of the various recommender models have been presented as a solution to the existing issues in recommendation systems. Metrics for evaluation and the need for diversity and novelty in recommender systems have also been discussed. Future research directions concerning mobile and IoT based, context-aware recommender systems and the effectiveness of Deep Learning models and how Transfer Learning could address the major drawbacks of recommender systems have also been discussed.