A Collaborative Filtering-Based Recommender Systems approach for Multifarious applications

Recommender systems are crucial in today's IT landscape, enhancing user experiences in various industries. Collaborative filtering (CF) is a key approach, using historical interaction data to predict user preferences. This paper presents CF advancements, with a focus on latent factor models that represent users and items in a compact feature space. It also addresses sparsity issues with techniques like neighborhood-based approaches and content augmentation. Contextual CF, which incorporates temporal and contextual dynamics, is explored through methods like matrix factorization with side information and session -based recommendation. Evaluation metrics such as MAE and RMSE, along with novel ranking -based metrics, provide a comprehensive assessment of recommendation quality. In this paper we outline cutting-edge CF techniques, showcasing their mechanisms and applications and were able to achieve accurate recommendations of almost 90% using MAE and RMSE metrics. By integrating latent factor modeling, sparsity mitigation, contextual enrichment, and advanced evaluation, it paves the way for the next generation of personalized recommendation systems, tailored to meet evolving demands in modern information environments.