LiMe: Linear Methods for Pseudo-Relevance Feedback

Retrieval effectiveness has been traditionally pursued by improving the ranking models and by enriching the pieces of evidence about the information need beyond the original query. A successful method for producing improved rankings consists in expanding the original query. Pseudo-relevance feedback (PRF) has proved to be an effective method for this task in the absence of explicit user's judgements about the initial ranking. This family of techniques obtains expansion terms using the top retrieved documents yielded by the original query. PRF techniques usually exploit the relationship between terms and documents or terms and queries. In this paper, we explore the use of linear methods for pseudo-relevance feedback. We present a novel formulation of the PRF task as a matrix decomposition problem which we called LiMe. This factorisation involves the computation of an inter-term similarity matrix which is used for expanding the original query. We use linear least squares regression with regularisation to solve the proposed decomposition with non-negativity constraints. We compare LiMe on five datasets against strong state-of-the-art baselines for PRF showing that our novel proposal achieves improvements in terms of MAP, nDCG and robustness index.