Item-Based Top-N Recommendation Algorithms

Mukund Deshpande and George Karypis
ACM Transactions on Information Systems. Volume 22, Issue 1, pp. 143 - 177, 2004
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The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems - a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques 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 similarity between a basket of items and a candidate recommender item. Our experimental evaluation on nine real datasets show that the proposed item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.

This is an expanded version of the original CIKM01 SUGGEST paper that shows that item-based schemes can be improved by incorporating more complex rules.
Research topics: Collaborative filtering | SUGGEST