@inproceedings{wang-etal-2025-integrating,
title = "Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation",
author = "Wang, Siyu and
Jiang, Jianhui and
Qiu, Jiangtao and
Dai, Shengran",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.153/",
pages = "2236--2245",
abstract = "Recent studies have demonstrated that cross-domain recommendation (CDR) effectively addresses the cold-start problem. Most approaches rely on transfer functions to generate user representations from the source to the target domain. Although these methods substantially enhance recommendation performance, they exhibit certain limitations, notably the frequent oversight of similarities in user preferences, which can offer critical insights for training transfer functions. Moreover, existing methods typically derive user preferences from historical purchase records or reviews, without considering that preferences operate at three distinct levels: category, brand, and aspect, each influencing decision-making differently. This paper proposes a model that integrates the preferences from coarse to fine levels to improve recommendations for cold-start users. The model leverages historical data from the source domain and external memory networks to generate user representations across different preference levels. A meta-network then transfers these representations to the target domain, where user-item ratings are predicted by aggregating the diverse representations. Experimental results demonstrate that our model outperforms state-of-the-art approaches in addressing the cold-start problem on three CDR tasks."
}
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<abstract>Recent studies have demonstrated that cross-domain recommendation (CDR) effectively addresses the cold-start problem. Most approaches rely on transfer functions to generate user representations from the source to the target domain. Although these methods substantially enhance recommendation performance, they exhibit certain limitations, notably the frequent oversight of similarities in user preferences, which can offer critical insights for training transfer functions. Moreover, existing methods typically derive user preferences from historical purchase records or reviews, without considering that preferences operate at three distinct levels: category, brand, and aspect, each influencing decision-making differently. This paper proposes a model that integrates the preferences from coarse to fine levels to improve recommendations for cold-start users. The model leverages historical data from the source domain and external memory networks to generate user representations across different preference levels. A meta-network then transfers these representations to the target domain, where user-item ratings are predicted by aggregating the diverse representations. Experimental results demonstrate that our model outperforms state-of-the-art approaches in addressing the cold-start problem on three CDR tasks.</abstract>
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%0 Conference Proceedings
%T Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation
%A Wang, Siyu
%A Jiang, Jianhui
%A Qiu, Jiangtao
%A Dai, Shengran
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-integrating
%X Recent studies have demonstrated that cross-domain recommendation (CDR) effectively addresses the cold-start problem. Most approaches rely on transfer functions to generate user representations from the source to the target domain. Although these methods substantially enhance recommendation performance, they exhibit certain limitations, notably the frequent oversight of similarities in user preferences, which can offer critical insights for training transfer functions. Moreover, existing methods typically derive user preferences from historical purchase records or reviews, without considering that preferences operate at three distinct levels: category, brand, and aspect, each influencing decision-making differently. This paper proposes a model that integrates the preferences from coarse to fine levels to improve recommendations for cold-start users. The model leverages historical data from the source domain and external memory networks to generate user representations across different preference levels. A meta-network then transfers these representations to the target domain, where user-item ratings are predicted by aggregating the diverse representations. Experimental results demonstrate that our model outperforms state-of-the-art approaches in addressing the cold-start problem on three CDR tasks.
%U https://aclanthology.org/2025.coling-main.153/
%P 2236-2245
Markdown (Informal)
[Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation](https://aclanthology.org/2025.coling-main.153/) (Wang et al., COLING 2025)
ACL