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
this https URL

为了解决会话推荐中 ID 模式与文本模式平衡的问题，本研究基于 AlterRec 提出了一种新的训练策略，通过分离 ID 和文本的训练，避免了平衡问题，并设计了一种促进两种模式互相学习和更有效地整合文本的策略。实验证明 AlterRec 在会话推荐中的有效性。