A Trustable Recommender System for Web Content

This paper presents three principles for building a Web recommender system that can be trusted to provide valuable page recommendations to Web users. We also demonstrate the implementation of these principles in an effective complete-Web recommender system — WebICLite. WebICLite is able to predict relevant pages based on a learned model, whose parameters are estimated from a labelled corpus. Data from a recent user study demonstrate that the prediction model can recommend previously unseen pages of high relevance from anywhere on the Web for any user.