Sequential recommendations (SR) predict users' future interactions based on their historical behavior. The rise of Large Language Models (LLMs) has brought powerful generative and reasoning capabilities, significantly enhancing SR performance, while Multimodal LLMs (MLLMs) further extend this by introducing data like images and interactive relationships. However, critical issues remain, i.e., (a) Suboptimal item representations caused by lengthy and redundant descriptions, leading to inefficiencies in both training and inference; (b) Modality-related cognitive bias, as LLMs are predominantly pretrained on textual data, limiting their ability to effectively integrate and utilize non-textual modalities; (c) Weakening sequential perception in long interaction sequences, where attention mechanisms struggle to capture earlier interactions, hindering the modeling of long-range dependencies. To address these issues, we propose Speeder, an efficient MLLM-based paradigm for SR featuring three key innovations: 1) Multimodal Representation Compression (MRC), which condenses item attributes into concise yet informative tokens, reducing redundancy and computational cost; 2) Modality-aware Progressive Optimization (MPO), enabling gradual learning of multimodal representations; 3) Sequential Position Awareness Enhancement (SPAE), improving the LLM's capability to capture both relative and absolute sequential dependencies in long interaction sequences. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of Speeder. Speeder increases training speed to 250% of the original while reducing inference time to 25% on the Amazon dataset.

翻译：序列推荐（SR）基于用户的历史行为预测其未来的交互。大语言模型（LLM）的兴起带来了强大的生成与推理能力，显著提升了SR的性能，而多模态大语言模型（MLLM）通过引入图像和交互关系等数据进一步扩展了这一能力。然而，关键问题依然存在：（a）由冗长冗余描述导致的次优项目表示，降低了训练与推理效率；（b）模态相关的认知偏差，由于LLM主要在文本数据上预训练，限制了其有效整合和利用非文本模态的能力；（c）长交互序列中的序列感知弱化，注意力机制难以捕捉早期交互，阻碍了长距离依赖的建模。为解决这些问题，我们提出了Speeder，一种基于MLLM的高效SR范式，具备三项关键创新：1）多模态表示压缩（MRC），将项目属性压缩为简洁且信息丰富的令牌，减少冗余与计算成本；2）模态感知渐进优化（MPO），实现多模态表示的渐进学习；3）序列位置感知增强（SPAE），提升LLM在长交互序列中捕捉相对与绝对序列依赖的能力。在真实数据集上的大量实验验证了Speeder的有效性与高效性。在Amazon数据集上，Speeder将训练速度提升至原始的250%，同时将推理时间降低至25%。