Filtering Discomforting Recommendations with Large Language Models Article Swipe

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Jiahao Liu , Yiyang Shao , Peng Zhang , Dongsheng Li , Hansu Gu , Chao Chen , Longzhi Du , Tun Lü , Ning Gu ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.05411

Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content. To address this, we first conducted a formative study to understand users' practices and expectations regarding discomforting recommendation filtering. Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile. Based on the edited profile, DiscomfortFilter facilitates the discomforting recommendations filtering in a plug-and-play manner, maintaining flexibility and transparency. The constructed preference profile improves LLM reasoning and simplifies user alignment, enabling a 3.8B open-source LLM to rival top commercial models in an offline proxy task. A one-week user study with 24 participants demonstrated the effectiveness of DiscomfortFilter, while also highlighting its potential impact on platform recommendation outcomes. We conclude by discussing the ongoing challenges, highlighting its relevance to broader research, assessing stakeholder impact, and outlining future research directions.

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Masking (illustration) Preference Computer science Information retrieval Mathematics Art Statistics Visual arts

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.05411
PDF: https://arxiv.org/pdf/2410.05411
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4403346233

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DOI: https://doi.org/10.48550/arxiv.2410.05411

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Title: Filtering Discomforting Recommendations with Large Language Models

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-10-07

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Authors: Jiahao Liu, Yiyang Shao, Peng Zhang, Dongsheng Li, Hansu Gu, Chao Chen, Longzhi Du, Tun Lü, Ning Gu

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Landing page: https://arxiv.org/abs/2410.05411

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PDF URL: https://arxiv.org/pdf/2410.05411

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Concepts: Masking (illustration), Preference, Computer science, Information retrieval, Mathematics, Art, Statistics, Visual arts

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Cited by: 1

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Citations by year (recent): 2025: 1

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Related works (count): 10

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