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Title: Search Result Diversification Using Query Aspects as Bottlenecks
We address some of the limitations of coverage-based search result diversification models, which often consist of separate components and rely on external systems for query aspects. To overcome these challenges, we introduce an end-to-end learning framework called DUB. Our approach preserves the intrinsic interpretability of coverage-based methods while enhancing diversification performance. Drawing inspiration from the information bottleneck method, we propose an aspect extractor that generates query aspect embeddings optimized as information bottlenecks for the task of diversified document re-ranking. Experimental results demonstrate that DUB outperforms state-of-the-art diversification models.  more » « less
Award ID(s):
2106282
PAR ID:
10542772
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
ACM
Date Published:
ISBN:
9798400701245
Page Range / eLocation ID:
3040 to 3051
Format(s):
Medium: X
Location:
Birmingham United Kingdom
Sponsoring Org:
National Science Foundation
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