Improving collaborative filtering recommendations by estimating user preferences from clickstream data

For practical applications of collaborative filtering, we need a user-item rating matrix that encodes user preferences for items. However, estimation of user preferences is inevitably affected by some degree of noise, which can markedly degrade the recommender performance. The primary aim of this research is to obtain a high-quality rating matrix by the effective use of clickstream data, which are a record of a user's page view (PV) history on an e-commerce site. To this end, we use the shape-restricted optimization model for estimating item-choice probabilities from the recency and frequency of each user's previous PVs. Experimental results based on real-world clickstream data demonstrate that higher recommender performance is achieved with our method than with baseline methods for constructing a rating matrix. Moreover, high recommender performance is maintained by our shape-restricted estimation even when only a limited number of training samples are available.