Modern ad serving systems can benefit when allowed to accumu- late user information and use it as part of the serving algorithm. However, this often does not coincide with how the web is used. Many domains will see users for only brief interactions, as users enter a domain through a search result or social media link and then leave. Having access to little or no user information and no ability to assemble a user profile over a prolonged period of use, we would still like to leverage the information we have to the best of our ability. In this paper we attempt several methods of improving ad serving for occasional users, including leveraging user information that is still available, content analysis of the page, information about the page’s content generators and historical breakdown of visits to the page. We compare and combine these methods in a framework of a collaborative filtering algorithm, test them on real data collected from Yahoo Answers, and achieve significant improvements over baseline algorithms.