Collaborative pseudo-relevance feedback

• We introduce a novel approach to pseudo-relevance feedback in information retrieval.
• We describe an adaptive tuning method automatically sets algorithmic parameters.
• Our method outperforms conventional pseudo-relevance feedback.
• Our method is quite stable regardless of the underlying retrieval model.

Pseudo-relevance feedback (PRF) is a technique commonly used in the field of information retrieval. The performance of PRF is heavily dependent upon parameter values. When relevance judgements are unavailable, these parameters are difficult to set. In the following paper, we introduce a novel approach to PRF inspired by collaborative filtering (CF). We also describe an adaptive tuning method which automatically sets algorithmic parameters. In a multi-stage evaluation using publicly available datasets, our technique consistently outperforms conventional PRF, regardless of the underlying retrieval model.