Query Term Weighting based on Query Performance Prediction
This work addresses search re-ranking for information retrieval systems, but it is incremental as it builds on existing QPP methods.
The paper tackles the problem of improving search re-ranking by weighting query terms based on their predicted impact on query performance, demonstrating effectiveness using state-of-the-art QPP methods on TREC corpora.
This work presents a general query term weighting approach based on query performance prediction (QPP). To this end, a given term is weighed according to its predicted effect on query performance. Such an effect is assumed to be manifested in the responses made by the underlying retrieval method for the original query and its (simple) variants in the form of a single-term expanded query. Focusing on search re-ranking as the underlying application, the effectiveness of the proposed term weighting approach is demonstrated using several state-of-the-art QPP methods evaluated over TREC corpora.