銆怲opic銆慡electively Acquiring Ratings for Product Recommendation
銆怱peaker銆慫an Huang
銆怲ime銆2008-5-21 11:00-12:00
銆怴enue銆慠oom 401, Weilun Building
銆怢anguage銆慐nglish/Chinese
銆怬rganizer銆慏epartment of Management Science and Engineering
銆怲arget Audience銆
銆怋ackgound Information銆
Abstract:
Accurate prediction of customer preferences on products is the key to any
recommender systems to realize its promised strategic value such as improved
customer satisfaction and therefore enhanced loyalty. In this paper, we
propose proactively acquiring ratings from customers for a newly introduced
product to quickly improve the accuracy of the predicted ratings generated
by a collaborative filtering recommendation algorithm for the entire
customer population. We formally introduce the problem of identifying the
most informative ratings to acquire and termed it as the product rating
acquisition problem. We proposed an active learning sampling method for this
problem that is generic to any recommendation algorithms. Using the Netflix
Prize dataset, we experimented with our proposed method, a uniform random
sampling method, and a degree-based sampling method that is biased toward
customers with large numbers of ratings for the user-based and item-based
neighborhood recommendation algorithms. The experimental results showed
that even with the random sampling method, acquiring 10% of all ratings in
addition to a randomly selected 10% initial ratings achieved 4.5% improvement on overall rating prediction accuracy of the movie. In addition, our proposed active learning algorithm consistently outperformed the
random and degree-based sampling for the better-performing item-based algorithm
and achieved about 8% improvement by acquiring 10% of the ratings.
Bio:
Zan Huang is an Assistant Professor at the Department Supply Chain and Information Systems, Smeal College of Business, the Pennsylvania State University. He received Ph.D. in Management Information Systems from the
University of Arizona and B.Eng. in Management Information Systems from Tsinghua University, Beijing. His primary research interest is data analysis and modeling for recommender systems, customer relationship management,
scientific and technology innovations, process management, and financial market. He has particularly focused on working with network data and developing network modeling/graph analysis methods. He has published in Management Science, ACM Transactions on Information Systems, IEEE Intelligent Systems, Decision Support Systems, Journal of the American
Society for Information Science, Journal of Nanoparticle Research, and ACM Journal on Educational Resources in Computing. He has been teaching MIS courses in the areas of System Analysis and Design, Business Data Communications, Introduction to Management Information Systems, and Introduction to Basic Operations Management.
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