Selectively acquiring ratings for product recommendation

Accurate prediction of customer preferences on products is the key to any recommender systems to realize its promised strategic values 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 algori...

• Type: Conference
• Year: 2007
• Where: ACMICEC
• Authors: Zan Huang
• Keywords: ACMICEC 2007, ECommerce, Learning Sampling Method, Recommendation Algorithms, Sampling Method