@inproceedings{10.1145/3583780.3615050,
author = {Yu, Puxuan and Rahimi, Razieh and Huang, Zhiqi and Allan, James},
title = {Search Result Diversification Using Query Aspects as Bottlenecks},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615050},
doi = {10.1145/3583780.3615050},
abstract = {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.},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {3040–3051},
numpages = {12},
keywords = {joint ranking and explanation, query aspects, search result diversification},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}