A Study of Collection-Based Features for Adapting the Balance Parameter in Pseudo Relevance Feedback.

Pseudo-relevance feedback (PRF) is an effective technique to improve the ad-hoc retrieval performance. For PRF methods, how to optimize the balance parameter between the original query model and feedback model is an important but difficult problem. Traditionally, the balance parameter is often manually tested and set to a fixed value across collections and queries. However, due to the difference among collections and individual queries, this parameter should be tuned differently. Recent research has studied various query based and feedback documents based features to predict the optimal balance parameter for each query on a specific collection, through a learning approach based on logistic regression. In this paper, we hypothesize that characteristics of collections are also important for the prediction. We propose and systematically investigate a series of collection- based features for queries, feedback documents and candidate expansion terms. The experiments show that our method is competitive in improving retrieval performance and particularly for cross-collection prediction, in comparison with the state-of-the-art approaches