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

TL;DR

This paper introduces a novel e-commerce long-tail product recommendation system leveraging large-scale language models to improve semantic understanding and user interest modeling, significantly enhancing recommendation accuracy for sparse data items.

Contribution

It proposes an integrated recommendation mechanism combining LLM-based semantic embeddings with user behavior modeling, addressing data sparsity and cold-start challenges in long-tail recommendations.

Findings

Outperforms baseline models in recall (+12%) and hit rate (+9%)

Improves user coverage by 15% for long-tail products

Demonstrates effective use of LLMs for semantic content interpretation

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.