43rd International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Hırvatistan, 28 Eylül - 02 Ekim 2020, ss.186-191
Memory-based classification techniques are commonly used for modeling recommendation problems. They rely on the intuition that similar users and/or items behave similarly, facilitating user-toitem, item-to-item, or user-to-user proximities. A significant drawback of memory-based classification techniques is that they perform poorly with large scale data. Thus, using the off-the-shelf classification techniques for recommendation problems generally lead to impractical computational costs.