Recommender Systems Using the Kappa Algorithm

Recommender systems play a crucial role in personalizing user experiences across various platforms. Traditional matrix factorization techniques like Singular Value Decomposition (SVD) have been widely adopted for rating prediction, but they often struggle with sparse datasets and cold-start problems. In this work, we explore the application of the Kappa algorithm, a similarity-based approach, to improve accuracy and robustness in such scenarios. The Kappa algorithm directly leverages observed rating similarities between users, bypassing the need for latent factors and allowing for fine-tuned control of personalization through the hyper-paramater <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex>. Through cross-validation experiments on the MovieLens 100K dataset, we demonstrate that the Kappa algorithm achieves competitive prediction performance. Our findings highlight the potential of the Kappa method for improving recommendation quality, particularly in sparse environments where traditional methods fall short.