Effective latent space graph-based re-ranking model with global consistency

Recently the re-ranking algorithms have been quite popular for web search and data mining. However, one of the issues is that those algorithms treat the content and link information individually. Inspired by graph-based machine learning algorithms, we propose a novel and general framework to model the re-ranking algorithm, by regularizing the smoothness of ranking scores over the graph, along with a regularizer on the initial ranking scores (which are obtained by the base ranker). The intuition behind the model is the global consistency over the graph: similar entities are likely to have the same ranking scores with respect to a query. Our approach simultaneously incorporates the content with other explicit or implicit link information in a latent space graph. Then an effective unified re-ranking algorithm is performed on the graph with respect to the query. To illustrate our methodology, we apply the framework to literature retrieval and expert finding applications on DBLP bibliography data. We compare the proposed method with the initial language model method and another PageRank-style re-ranking method. Also, we evaluate the proposed method with varying graphs and settings. Experimental results show that the improvement in our proposed method is consistent and promising.