Towards a context-sensitive online newspaper

Abstract

We give a detailed account of our experiences in implementing a personalized online newspaper that draws-among other hints-on the context of the user. At the algorithmic core of our framework lies a machine learning model that incorporates numerous features of the eligible articles and the user's current situation. Some of the most important design decisions, however, concern the presentation of suggestions, the collection of explicit and implicit feedback, as well as diversity of the recommendations. We present numerical results obtained during the pilot phase of the project that address a number of these concerns and end with a discussion of open questions and future directions.