Query-Document Relevance Topic Models

In this paper, we aim to deal with the deficiency of current information retrieval models by integrating the concept of relevance into the generation model from different topical aspects of the query. We study a series of relevance-dependent topic models. These models are adapted from the latent Dirichlet allocation model. They are distinguished by how the notation of query-document relevance, which is critical in information retrieval, is introduced in the modeling framework. Approximate yet efficient parameter estimation methods based on the Gibbs sampling technique are employed for parameter estimation. The results of experiments evaluated on the Text REtrieval Conference Corpus in terms of the mean average precision (mAP) demonstrate the superiority of the proposed models.