Session-based recommendation has gained increasing attention in recent years, with its aim to offer tailored suggestions based on users' historical behaviors within sessions. To advance this field, a variety of methods have been developed, with ID-based approaches typically demonstrating promising performance. However, these methods often face challenges with long-tail items and overlook other rich forms of information, notably valuable textual semantic information. To integrate text information, various methods have been introduced, mostly following a naive fusion framework. Surprisingly, we observe that fusing these two modalities does not consistently outperform the best single modality by following the naive fusion framework. Further investigation reveals an potential imbalance issue in naive fusion, where the ID dominates and text modality is undertrained. This suggests that the unexpected observation may stem from naive fusion's failure to effectively balance the two modalities, often over-relying on the stronger ID modality. This insight suggests that naive fusion might not be as effective in combining ID and text as previously expected. To address this, we propose a novel alternative training strategy AlterRec. It separates the training of ID and text, thereby avoiding the imbalance issue seen in naive fusion. Additionally, AlterRec designs a novel strategy to facilitate the interaction between the two modalities, enabling them to mutually learn from each other and integrate the text more effectively. Comprehensive experiments demonstrate the effectiveness of AlterRec in session-based recommendation. The implementation is available at https://github.com/Juanhui28/AlterRec.

翻译：会话推荐近年来受到广泛关注，其目标是根据用户在会话中的历史行为提供个性化推荐。为推进该领域发展，研究者已开发多种方法，其中基于ID的方法通常表现出色。然而，这些方法常面临长尾物品挑战，并忽视其他丰富的信息形式，尤其是宝贵的文本语义信息。为整合文本信息，研究者引入多种方法，大多遵循朴素融合框架。令人惊讶的是，我们发现遵循朴素融合框架融合这两种模态并非始终优于最佳单模态性能。进一步探究揭示朴素融合存在潜在不平衡问题：ID模态占主导地位而文本模态训练不足。这表明该意外现象可能源于朴素融合未能有效平衡两种模态，往往过度依赖更强的ID模态。该发现暗示朴素融合在结合ID与文本方面的效果可能不如预期。为此，我们提出新型交替训练策略AlterRec，通过分离ID与文本的训练避免朴素融合中的不平衡问题。此外，AlterRec设计新颖策略促进两模态交互，使其相互学习并更有效地整合文本。大量实验证明AlterRec在会话推荐中的有效性。实现代码见https://github.com/Juanhui28/AlterRec。