Recommender Systems for Business Intelligence
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Prof. Jie Lu |
Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Australia |
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A recommender system aims to provide online users with personalized recommendations about products or services to handle the increasing problem of online information overload and to improve the management of customer relations. An increasing number of real-world applications of recommender systems have recently been successfully developed, demonstrating that recommender systems now provide business with unprecedented opportunities. At the same time, real-world reports show that recommender systems continue to face challenges in handling prediction accuracy, sparsity, cold start, and uncertainty issues. This talk first systematically examines the recent development of recommender systems through three dimensions: recommendation methods, recommender systems software, and real-world application domains. It then explains how fuzzy technique can effectively support recommendation methods to handle the current challenging issues. It particularly describes the up-to-date applications of recommender systems in e-government, e-business, e-commerce, e-learning, e-tourism, and e-group activities. This talk will provide researchers and professionals with state-of-the-art knowledge and techniques to the development of recommender system methods and applications, supported by fuzzy techniques, for business intelligence. |
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