@inproceedings{bassani-etal-2024-denoising,
    title = "Denoising Attention for Query-aware User Modeling",
    author = "Bassani, Elias  and
      Kasela, Pranav  and
      Pasi, Gabriella",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-naacl.153/",
    doi = "10.18653/v1/2024.findings-naacl.153",
    pages = "2368--2380",
    abstract = "Personalization of search results has gained increasing attention in the past few years, also thanks to the development of Neural Networks-based approaches for Information Retrieval. Recent works have proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query.This approach allows giving more importance to the user{'}s interests related to the current search performed by the user.In this paper, we discuss some shortcomings of the Attention mechanism when employed for personalization and introduce a novel Attention variant, the Denoising Attention, to solve them.Denoising Attention adopts a robust normalization scheme and introduces a filtering mechanism to better discern among the user-related data those helpful for personalization.Experimental evaluation shows improvements in MAP, MRR, and NDCG above 15{\%} w.r.t. other Attention variants at the state-of-the-art."
}