Deshpande, MukundKarypis, George2020-09-022020-09-022003-01-20https://hdl.handle.net/11299/215545The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development ofrecommender systems-a personalized information filtering technology used to identify a set of N items that willbe of interest to a certain user. User-based collaborative filtering is the most successful technology for buildingrecommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, thecomputational complexity of these methods grows linearly with the number of customers that in typical commercialapplications can grow to be several millions. To address these scalability concerns item-based recommendationtechniques have been developed that analyze the user-item matrix to identify relations between the different items,and use these relations to compute the list of recommendations. In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similaritybetween a basket of items and a candidate recommenderitem. Our experimental evaluation on nine real datasets show that the proposed item-based algorithms areup to two orders of magnitude faster than the traditionaluser-neighborhood based recommender systems and providerecommendations with comparable or better quality.en-USReport