TY - GEN
T1 - Utilizing pseudo-relevance feedback in fusion-based retrieval
AU - Roitman, Haggai
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - The usage of positive relevance feedback in fusion-based retrieval was previously shown to be very useful. Yet, in many retrieval usecases, no actual relevance feedback may be available. With the absence of relevance data, pseudo-relevance feedback models have been suggested as an alternative. Encouraged by the previous success of using positive relevance feedback in fusion-based retrieval, in this work, we study the usage of pseudo-relevance feedback in this setting as well. We build on top of an existing approach that was originally designed for utilizing positive relevance feedback and adapt it to pseudo-relevance feedback. To this end, we propose a novel approach for estimating document (pseudo) relevance labels. Our labeling approach is better tailored to the fusion-based retrieval setting and provides favorable retrieval quality results.
AB - The usage of positive relevance feedback in fusion-based retrieval was previously shown to be very useful. Yet, in many retrieval usecases, no actual relevance feedback may be available. With the absence of relevance data, pseudo-relevance feedback models have been suggested as an alternative. Encouraged by the previous success of using positive relevance feedback in fusion-based retrieval, in this work, we study the usage of pseudo-relevance feedback in this setting as well. We build on top of an existing approach that was originally designed for utilizing positive relevance feedback and adapt it to pseudo-relevance feedback. To this end, we propose a novel approach for estimating document (pseudo) relevance labels. Our labeling approach is better tailored to the fusion-based retrieval setting and provides favorable retrieval quality results.
UR - https://www.scopus.com/pages/publications/85063531151
U2 - 10.1145/3234944.3234969
DO - 10.1145/3234944.3234969
M3 - Conference contribution
AN - SCOPUS:85063531151
T3 - ICTIR 2018 - Proceedings of the 2018 ACM SIGIR International Conference on the Theory of Information Retrieval
SP - 203
EP - 206
BT - ICTIR 2018 - Proceedings of the 2018 ACM SIGIR International Conference on the Theory of Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2018
Y2 - 14 September 2018 through 17 September 2018
ER -