TY - GEN
T1 - A probabilistic fusion framework
AU - Anava, Yael
AU - Shtok, Anna
AU - Kurland, Oren
AU - Rabinovich, Ella
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.
AB - There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.
UR - http://www.scopus.com/inward/record.url?scp=84996538445&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983739
DO - 10.1145/2983323.2983739
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AN - SCOPUS:84996538445
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1463
EP - 1472
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Y2 - 24 October 2016 through 28 October 2016
ER -