Recommendations meet web browsing: Enhancing Collaborative

Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. Here we investigate how CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals’ interests. We introduce two approaches to enhancing user modeling using this data. Our approaches preserve the privacy of individuals while significantly enhancing model accuracy.

We present extensive experimentation based on one-class, implicit feedback matrix factorization. We do not assume the existence of explicit ratings, but rather rely on unweighted, positive signals of the kind available in most commercial contexts. We demonstrate the value of our approach on two real datasets each comprising of the activities of tens of thousands of individuals. The first dataset details the downloads of Windows Phone 8 mobile applications and the second – item views in an online retail store. Both datasets are enhanced using anonymized Internet browsing logs.

Our results show that prediction accuracy is improved by up to 72%. This improvement is largest when building a model which can predict for the entire catalog of items, not just popular ones. Finally, we discuss additional benefits of our approach, which include: improved recommendations for users with few past purchases, and enabling recommendations based on short-term purchase intent.