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Research Article Open Access
Cluster-based recommendation is best thought of as a variant on user-based recommendation. Instead of recommending items to users, items are recommended to clusters of similar users. This entails a pre processing phase, in which all users are partitioned into clusters. Recommendations are then produced for each cluster, such that the recommended items are most interesting to the largest number of users. The upside of this approach is that recommendation is fast at runtime because almost everything is pre computed. In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional modelbased recommender systems, as suggested in Adomavicius and Tuzhilin can address this problem. We introduce Recommendation Engine by Conjoint Decomposition of items and Users (RECONDITUS)-a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set.