Enhancing news organization for convenient retrieval and browsing

Abstract

To facilitate users to access news quickly and comprehensively, we design a news search and browsing system named GeoVisNews, in which the news elements of “Where”, “Who”, “What” and “When” are enhanced via news geo-localization, image enrichment and joint ranking, respectively. For news geo-localization, an Ordinal Correlation Consistent Matrix Factorization (OCCMF) model is proposed to maintain the relevance rankings of locations to a specific news document and simultaneously capture intra-relations among locations and documents. To visualize news, we develop a novel method to enrich news documents with appropriate web images. Specifically, multiple queries are first generated from news documents for image search, and then the appropriate images are selected from the collected web images by an intelligent fusion approach based on multiple features. Obtaining the geo-localized and image enriched news resources, we further employ a joint ranking strategy to provide relevant, timely and popular news items as the answer of user searching queries. Extensive experiments on a large-scale news dataset collected from the web demonstrate the superior performance of the proposed approaches over related methods.