@inproceedings{li-etal-2025-enhancing-id,
title = "Enhancing {ID} and Text Fusion via Alternative Training in Session-based Recommendation",
author = "Li, Juanhui and
Han, Haoyu and
Chen, Zhikai and
Shomer, Harry and
Jin, Wei and
Javari, Amin and
Liu, Hui",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.12/",
pages = "185--199",
ISBN = "979-8-89176-298-5",
abstract = "Session-based recommendation systems have attracted growing interest for their ability to provide personalized recommendations based on users' in-session behaviors. While ID-based methods have shown strong performance, they often struggle with long-tail items and overlook valuable textual information. To incorporate text information, various approaches have been proposed, generally employing a naive fusion framework. Interestingly, this approach often fails to outperform the best single-modality baseline. Further exploration indicates a potential imbalance issue in the naive fusion method, where the ID tends to dominate the training and the text is undertrained. This issue indicates that the naive fusion method might not be as effective in combining ID and text as once believed. To address this, we propose AlterRec, an alternative training framework that separates the optimization of ID and text to avoid the imbalance issue. AlterRec also designs an effective strategy to enhance the interaction between the two modalities, facilitating mutual interaction and more effective text integration. Extensive experiments demonstrate the effectiveness of AlterRec in session-based recommendation."
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<abstract>Session-based recommendation systems have attracted growing interest for their ability to provide personalized recommendations based on users’ in-session behaviors. While ID-based methods have shown strong performance, they often struggle with long-tail items and overlook valuable textual information. To incorporate text information, various approaches have been proposed, generally employing a naive fusion framework. Interestingly, this approach often fails to outperform the best single-modality baseline. Further exploration indicates a potential imbalance issue in the naive fusion method, where the ID tends to dominate the training and the text is undertrained. This issue indicates that the naive fusion method might not be as effective in combining ID and text as once believed. To address this, we propose AlterRec, an alternative training framework that separates the optimization of ID and text to avoid the imbalance issue. AlterRec also designs an effective strategy to enhance the interaction between the two modalities, facilitating mutual interaction and more effective text integration. Extensive experiments demonstrate the effectiveness of AlterRec in session-based recommendation.</abstract>
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%0 Conference Proceedings
%T Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
%A Li, Juanhui
%A Han, Haoyu
%A Chen, Zhikai
%A Shomer, Harry
%A Jin, Wei
%A Javari, Amin
%A Liu, Hui
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F li-etal-2025-enhancing-id
%X Session-based recommendation systems have attracted growing interest for their ability to provide personalized recommendations based on users’ in-session behaviors. While ID-based methods have shown strong performance, they often struggle with long-tail items and overlook valuable textual information. To incorporate text information, various approaches have been proposed, generally employing a naive fusion framework. Interestingly, this approach often fails to outperform the best single-modality baseline. Further exploration indicates a potential imbalance issue in the naive fusion method, where the ID tends to dominate the training and the text is undertrained. This issue indicates that the naive fusion method might not be as effective in combining ID and text as once believed. To address this, we propose AlterRec, an alternative training framework that separates the optimization of ID and text to avoid the imbalance issue. AlterRec also designs an effective strategy to enhance the interaction between the two modalities, facilitating mutual interaction and more effective text integration. Extensive experiments demonstrate the effectiveness of AlterRec in session-based recommendation.
%U https://aclanthology.org/2025.ijcnlp-long.12/
%P 185-199
Markdown (Informal)
[Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation](https://aclanthology.org/2025.ijcnlp-long.12/) (Li et al., IJCNLP-AACL 2025)
ACL
- Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer, Wei Jin, Amin Javari, and Hui Liu. 2025. Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 185–199, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.