Relevance Feedback using Latent Information

We present a novel relevance feedback (RF) method that uses not only the surface information in texts, but also the latent information contained therein. In the proposed method, we infer the latent topic distribution in user feedback and in each document in the search results using latent Dirichlet allocation, and then we modify the search results so that documents with a similar topic distribution to that of the feedback are re-ranked higher. Evaluation results show that our method is effective for both explicit and pseudo RF, and that it has the advantage of performing well even when only a small amount of user feedback is available.