Invention Grant
- Patent Title: Enhanced query performance prediction for information retrieval systems
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Application No.: US16236693Application Date: 2018-12-31
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Publication No.: US11275749B2Publication Date: 2022-03-15
- Inventor: Haggai Roitman , Shai Erera , Bar Weiner
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Patterson + Sheridan, LLP
- Main IPC: G06F16/00
- IPC: G06F16/00 ; G06F16/2457 ; G06N5/02 ; G06F16/248

Abstract:
Techniques are disclosed for query performance prediction (QPP) in the fusion-based retrieval setting. Symmetric list similarity measures used in traditional QPP techniques do not properly account for relevance-dependent aspects of the relationship between a given (base) reference list generated using an information retrieval technique and a final fused list generated using a fusion technique, as such a relationship is actually asymmetric. Embodiments more properly model the asymmetric relationship of reference and fused lists using an asymmetric co-relevance model that estimates, assuming a reference list contains relevant information, the odds that the fused list will be observed. In particular, the asymmetric co-relevance between a reference list and a fused list may be determined by adjusting a symmetric co-relevance of the reference list and the fused list using an odds ratio between the symmetric co-relevance of the reference list and the fused list to the reference list's own relevance.
Public/Granted literature
- US20200210438A1 ENHANCED QUERY PERFORMANCE PREDICTION FOR INFORMATION RETRIEVAL SYSTEMS Public/Granted day:2020-07-02
Information query