A Priori Relevance Based On Quality and Diversity Of Social Signals

Social signals (users' actions) associated with web resources (documents) can be considered as an additional information that can play a role to estimate a priori importance of the resource. In this paper, we are particularly interested in: first, showing the impact of signals diversity associated to a resource on information retrieval performance; second, studying the influence of their social networks origin on their quality. We propose to model these social features as prior that we integrate into language model. We evaluated the effectiveness of our approach on IMDb dataset containing 167438 resources and their social signals collected from several social networks. Our experimental results are statistically significant and show the interest of integrating signals diversity in the retrieval process.