Bayesian relevance feedback

Creator
Creator
Seonglae Cho
Created
Created
2025 Mar 10 15:28
Editor
Edited
Edited
2025 Mar 10 15:31
Refs
Refs
Optimum query Seeks to find a query that maximizes similarity to elevant documents while minimizing similarity with non-relevant documents
Let CrC_r denote set of relevant docs, Let CnrC_{nr} denote set of non-relevant docs.
qopt=arg maxp[sim(q,Cr)sim(q,Crn)]q_{opt} = \argmax_p [sim(q, C_r) - sim(q, C_{rn})]qopt=dCrdiCrdCrndiCrnq_{opt} = \frac{\sum_{d\in C_r} d_i}{|C_r|} - \frac{\sum_{d\in C_{rn}} d_i}{|C_{rn}|}
Compute the weight for term i using the log-odds ratio:
wi=log(pi(1qi)qi(1pi))w_i = \log\left(\frac{p_i\,(1-q_i)}{q_i\,(1-p_i)}\right)
The updated query vector is then given by:
qBRF=q0+[w1,w2,,wn]\vec{q}_{BRF} = \vec{q_0} + [w_1, w_2, \dots, w_n]
 
 
 
 
 

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