Topical PageRank: A Model of Scientific Expertise for Bibliographic Search

We model scientific expertise as a mixture of topics and authority. Authority is calculated based on the network properties of each topic network. ThemedPageRank, our combination of LDA-derived topics with PageRank differs from previous models in that topics influence both the bias and transition probabilities of PageRank. It also incorporates the age of documents. Our model is general in that it can be applied to all tasks which require an estimate of document–document, document– query, document–topic and topic–query similarities. We present two evaluations, one on the task of restoring the reference lists of 10,000 articles, the other on the task of automatically creating reading lists that mimic reading lists created by experts. In both evaluations, our system beats state-of-the-art, as well as Google Scholar and Google Search indexed againt the corpus. Our experiments also allow us to quantify the beneficial effect of our two proposed modifications to PageRank.