Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models

• URL: http://arxiv.org/abs/2506.06336v1
• Date: Sat, 31 May 2025 19:17:48 GMT
• Title: Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models
• Authors: Qingyi Lu, Haotian Lyu, Jiayun Zheng, Yang Wang, Li Zhang, Chengrui Zhou,
• Abstract summary: We propose a novel long-tail product recommendation mechanism that integrates product text descriptions and user behavior sequences using a large-scale language model (LLM)<n>Our work highlights the potential of LLMs in interpreting product content and user intent, offering a promising direction for future e-commerce recommendation systems.
• Score: 7.792622257477251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As e-commerce platforms expand their product catalogs, accurately recommending long-tail items becomes increasingly important for enhancing both user experience and platform revenue. A key challenge is the long-tail problem, where extreme data sparsity and cold-start issues limit the performance of traditional recommendation methods. To address this, we propose a novel long-tail product recommendation mechanism that integrates product text descriptions and user behavior sequences using a large-scale language model (LLM). First, we introduce a semantic visor, which leverages a pre-trained LLM to convert multimodal textual content such as product titles, descriptions, and user reviews into meaningful embeddings. These embeddings help represent item-level semantics effectively. We then employ an attention-based user intent encoder that captures users' latent interests, especially toward long-tail items, by modeling collaborative behavior patterns. These components feed into a hybrid ranking model that fuses semantic similarity scores, collaborative filtering outputs, and LLM-generated recommendation candidates. Extensive experiments on a real-world e-commerce dataset show that our method outperforms baseline models in recall (+12%), hit rate (+9%), and user coverage (+15%). These improvements lead to better exposure and purchase rates for long-tail products. Our work highlights the potential of LLMs in interpreting product content and user intent, offering a promising direction for future e-commerce recommendation systems.

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