MineRank: Leveraging users' latent roles for unsupervised collaborative information retrieval

Research on collaborative information retrieval (CIR) has shown the positive impact of collaboration on retrieval effectiveness in the case of complex or exploratory tasks. The synergic effect of accomplishing something greater than the sum of its individual components is reached through the gathering of collaborators’ complementary skills. However, these approaches often lack the consideration that collaborators might refine their skills and actions throughout the search session, and that a flexible system mediation guided by collaborators’ behaviors should dynamically optimize the search effectiveness. In this article, we propose a new unsupervised collaborative ranking algorithm which leverages collaborators’ actions for (1) mining their latent roles in order to extract their complementary search behaviors; and (2) ranking documents with respect to the latent role of collaborators. Experiments using two user studies with respectively 25 and 10 pairs of collaborators demonstrate the benefit of such unsupervised method driven by collaborators’ behaviors throughout the search session. Also, a qualitative analysis of the identified latent role is proposed to explain an over-learning noticed for one of the dataset.