@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}