Selecting a small number of products for effective user profiling in collaborative filtering

Collaborative filtering (CF) is one of the most widely used methods for personalized product recommendation at online stores. CF predicts users’ preferences on products using past data of users such as purchase records or their ratings on products. The prediction is then used for personalized recommendation so that products with highly estimated preference for each user are selected and presented. One of the most difficult issues in using CF is that it is often hard to collect sufficient amount of data for each user to estimate preferences accurately enough. In order to address this problem, this research studies how we can gain the most information about each user by collecting data on a very small number of selected products, and develops a method for choosing a sequence of such products tailored to each user based on metrics from information theory and correlation-based product similarity. The effectiveness of the proposed methods is tested using experiments with the MovieLens dataset.