@inproceedings{cheng-etal-2025-learning,
title = "Learning User Common Interests for Unseen Group Recommendation",
author = "Cheng, Yu-Ting and
Hsiao, Pin-Hsin and
Fuh, Chiou-Shann and
Cheng, Pu-Jen",
editor = "Chang, Kai-Wei and
Lu, Ke-Han and
Yang, Chih-Kai and
Tam, Zhi-Rui and
Chang, Wen-Yu and
Wang, Chung-Che",
booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
month = nov,
year = "2025",
address = "National Taiwan University, Taipei City, Taiwan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.rocling-main.36/",
pages = "334--341",
ISBN = "979-8-89176-379-1",
abstract = "Previous studies on recommender systems have primarily focused on learning implicit preferences from individual user behaviors or enhancing recommendation performance by identifying similar users. However, in real-life scenarios, group decision-making is often required, such as when a group of friends decides which movie to watch together. Thus, discovering common interests has become a key research issue in group recommendation. The most straightforward approach to group recommendation is to model the past joint behaviors of a user group. Nevertheless, this method fails to handle newly formed groups with no historical interactions. To address this limitation, we apply Graph Convolutional Networks to capture high-order structural features within the user{--}item interaction graph, thereby uncovering the potential common interests of unseen groups. Experimental evaluations on three real-world datasets demonstrate the feasibility and effectiveness of the proposed method."
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<abstract>Previous studies on recommender systems have primarily focused on learning implicit preferences from individual user behaviors or enhancing recommendation performance by identifying similar users. However, in real-life scenarios, group decision-making is often required, such as when a group of friends decides which movie to watch together. Thus, discovering common interests has become a key research issue in group recommendation. The most straightforward approach to group recommendation is to model the past joint behaviors of a user group. Nevertheless, this method fails to handle newly formed groups with no historical interactions. To address this limitation, we apply Graph Convolutional Networks to capture high-order structural features within the user–item interaction graph, thereby uncovering the potential common interests of unseen groups. Experimental evaluations on three real-world datasets demonstrate the feasibility and effectiveness of the proposed method.</abstract>
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%0 Conference Proceedings
%T Learning User Common Interests for Unseen Group Recommendation
%A Cheng, Yu-Ting
%A Hsiao, Pin-Hsin
%A Fuh, Chiou-Shann
%A Cheng, Pu-Jen
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F cheng-etal-2025-learning
%X Previous studies on recommender systems have primarily focused on learning implicit preferences from individual user behaviors or enhancing recommendation performance by identifying similar users. However, in real-life scenarios, group decision-making is often required, such as when a group of friends decides which movie to watch together. Thus, discovering common interests has become a key research issue in group recommendation. The most straightforward approach to group recommendation is to model the past joint behaviors of a user group. Nevertheless, this method fails to handle newly formed groups with no historical interactions. To address this limitation, we apply Graph Convolutional Networks to capture high-order structural features within the user–item interaction graph, thereby uncovering the potential common interests of unseen groups. Experimental evaluations on three real-world datasets demonstrate the feasibility and effectiveness of the proposed method.
%U https://aclanthology.org/2025.rocling-main.36/
%P 334-341
Markdown (Informal)
[Learning User Common Interests for Unseen Group Recommendation](https://aclanthology.org/2025.rocling-main.36/) (Cheng et al., ROCLING 2025)
ACL
- Yu-Ting Cheng, Pin-Hsin Hsiao, Chiou-Shann Fuh, and Pu-Jen Cheng. 2025. Learning User Common Interests for Unseen Group Recommendation. In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 334–341, National Taiwan University, Taipei City, Taiwan. Association for Computational Linguistics.