Break the ID-Language Barrier: An Adaption Framework for Sequential Recommendation

• URL: http://arxiv.org/abs/2411.18262v1
• Date: Wed, 27 Nov 2024 11:59:44 GMT
• Title: Break the ID-Language Barrier: An Adaption Framework for Sequential Recommendation
• Authors: Xiaohan Yu, Li Zhang, Xin Zhao, Yue Wang,
• Abstract summary: We propose IDLE-Adapter, a framework that integrates pre-trained ID embeddings, rich in domain-specific knowledge, into large language models. IDLE-Adapter acts as a bridge, transforming sparse user-item interaction data into dense, LLM-compatible representations.
• Score: 10.305878081909743
• License:
• Abstract: The recent breakthrough of large language models (LLMs) in natural language processing has sparked exploration in recommendation systems, however, their limited domain-specific knowledge remains a critical bottleneck. Specifically, LLMs lack key pieces of information crucial for sequential recommendations, such as user behavior patterns. To address this critical gap, we propose IDLE-Adapter, a novel framework that integrates pre-trained ID embeddings, rich in domain-specific knowledge, into LLMs to improve recommendation accuracy. IDLE-Adapter acts as a bridge, transforming sparse user-item interaction data into dense, LLM-compatible representations through a Pre-trained ID Sequential Model, Dimensionality Alignment, Layer-wise Embedding Refinement, and Layer-wise Distribution Alignment. Furthermore, IDLE-Adapter demonstrates remarkable flexibility by seamlessly integrating ID embeddings from diverse ID-based sequential models and LLM architectures. Extensive experiments across various datasets demonstrate the superiority of IDLE-Adapter, achieving over 10\% and 20\% improvements in HitRate@5 and NDCG@5 metrics, respectively, compared to state-of-the-art methods.

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