Learning a Model of a Web User’s Interests

Abstract

There are many recommender systems that are designed to help users find relevant information on the web. To produce recommendations that are relevant to an individual user, many of these systems first attempt to learn a model of the user’s browsing behavior. This paper presents a novel method for learning such a model from a set of annotated web logs—i.e., web logs that are augmented with the user’s assessment of whether each webpage is an information content (IC) page (i.e., contains the information required to complete her task). Our systems use this to learn what properties of a webpage, within a sequence, identify such IC-pages, and similarly what “browsing properties” characterize the words on such pages (“IC-words”). As these methods deal with properties of webpages (or of words), rather than specific URLs (words), they can be used anywhere throughout the web; i.e., they are not specific to a particular website, or a particular task. This paper also describes the enhanced browser, aie, that we designed and implemented for collecting these annotated web logs, and an empirical study we conducted to investigate the effectiveness of our approach. This empirical evidence shows that our approach, and our algorithms, work effectively.