Further Theoretical Study of Distribution Separation Method for Information Retrieval
This work provides incremental theoretical insights for researchers in information retrieval, focusing on feedback mechanisms.
The paper further studies the theoretical properties of the Distribution Separation Method (DSM) for information retrieval, proving equivalences between assumptions and showing that the EM algorithm in a mixture model can be simplified using DSM's linear separation algorithm, with empirical results provided.
Recently, a Distribution Separation Method (DSM) is proposed for relevant feedback in information retrieval, which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While DSM achieved a promising empirical performance, theoretical analysis of DSM is still need further study and comparison with other relative retrieval model. In this article, we first generalize DSM's theoretical property, by proving that its minimum correlation assumption is equivalent to the maximum (original and symmetrized) KL-Divergence assumption. Second, we also analytically show that the EM algorithm in a well-known Mixture Model is essentially a distribution separation process and can be simplified using the linear separation algorithm in DSM. Some empirical results are also presented to support our theoretical analysis.