Co-clustering neighborhood—based collaborative filtering framework using formal concept analysis

Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

“…To address the problem of large data volumes, researchers such as Paun et al [ 30 ] proposed the famous SlopeOne algorithm, which simplifies the regression function in collaborative filtering, significantly reducing the calculation time and storage requirements while achieving a recommendation effect equal to or even better than the collaborative filtering algorithm. Kataria & Batra [ 31 ] studied the co-clustering method, simultaneously clustering users and items, reducing computational complexity by looking for neighbors.…”