An Extended Relevance Model for Session Search
This work addresses session search for users by proposing an extended relevance model, which appears incremental as it builds on existing relevance modeling approaches.
The authors tackled the problem of improving session search performance by modeling dynamic user information needs, resulting in a significant performance boost.
The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance.