期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：3
DOI：10.15680/ijircce.2015.0303164
出版社：S&S Publications
摘要：Cluster-based recommendation is best thought of as a variant on user-based recommendation. Instead ofrecommending 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 therecommended items are most interesting to the largest number of users. The upside of this approach is thatrecommendation is fast at runtime because almost everything is pre computed. In this paper, we describe the problemof recommending conference sessions to attendees and show how novel extensions to traditional modelbasedrecommender systems, as suggested in Adomavicius and Tuzhilin can address this problem. Weintroduce Recommendation Engine by Conjoint Decomposition of items and Users (RECONDITUS)-a technique thatis an extension of preference-based recommender systems to recommend items from a new disjoint set to users from anew disjoint set.
