Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs

• URL: http://arxiv.org/abs/2509.02017v1
• Date: Tue, 02 Sep 2025 07:02:29 GMT
• Title: Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
• Authors: Yuhao Wang, Junwei Pan, Xinhang Li, Maolin Wang, Yuan Wang, Yue Liu, Dapeng Liu, Jie Jiang, Xiangyu Zhao,
• Abstract summary: Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions.<n>MME-SID integrates multimodal embeddings and quantized embeddings to mitigate embedding collapse.<n>Extensive experiments on three public datasets validate the superior performance of MME-SID.
• Score: 28.752042722391934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large language models (LLMs) have driven their adoption in SR. However, we identify two critical challenges in existing LLM-based SR methods: 1) embedding collapse when incorporating pre-trained collaborative embeddings and 2) catastrophic forgetting of quantized embeddings when utilizing semantic IDs. These issues dampen the model scalability and lead to suboptimal recommendation performance. Therefore, based on LLMs like Llama3-8B-instruct, we introduce a novel SR framework named MME-SID, which integrates multimodal embeddings and quantized embeddings to mitigate embedding collapse. Additionally, we propose a Multimodal Residual Quantized Variational Autoencoder (MM-RQ-VAE) with maximum mean discrepancy as the reconstruction loss and contrastive learning for alignment, which effectively preserve intra-modal distance information and capture inter-modal correlations, respectively. To further alleviate catastrophic forgetting, we initialize the model with the trained multimodal code embeddings. Finally, we fine-tune the LLM efficiently using LoRA in a multimodal frequency-aware fusion manner. Extensive experiments on three public datasets validate the superior performance of MME-SID thanks to its capability to mitigate embedding collapse and catastrophic forgetting. The implementation code and datasets are publicly available for reproduction: https://github.com/Applied-Machine-Learning-Lab/MME-SID.

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