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So, the next step is to see In each of these cases, the fbOrigWeight that controls the mixing of the original and feedback query is relatively fixed – learned on average from the training queries. We can explore whether we can reliably predict when to apply one model or another or the other. 

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to predict the fbOrigWeight mixing parameter via query performance prediction methods.

Query performance prediction

Adaptive feedback

One approach explored by Lv and Zhai (2009) is to learn a model to predict the expansion mixing weight. They found six features to be predictive of the feedback weight in a linear model:

• Divergence (QFBDiv_A): KL-divergence of query and feedback documents
• Query clarity (QEnt_R1): Relative entropy of the query compared to the collection
• Log query clarity (QEnt_R3): Log of the relative entropy of the query compared to the collection
• Feedback radius (FBRadius): average divergence between each document and the centroid of the feedback documents.
• Exponentiated feedback clarity (FBEnt_R2): Exponentiated relative entropy of feedback documents to the collection
• Topic model clarity (FBEnt_R3): Relative entropy of the feedback document topic model to the collection.

All of these predictors are post-retrieval predictors, some with significant overhead.

References

Lv, Y., & Zhai, C. (2009). Adaptive Relevance Feedback in Information Retrieval. In Proceedings of the 18th ACM Conference on Information and Knowledge Management (pp. 255–264). New York, NY, USA: ACM. http://doi.org/10.1145/1645953.1645988