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Journal of Big Data
AbstractRecommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work whiledoi:10.1186/s40537-022-00592-5 fatcat:t6fomj2vpff5ticjzwvezqw2di