TagLLM: A Fine-Grained Tag Generation Approach for Note Recommendation

TL;DR

TagLLM introduces a novel fine-grained tag generation method leveraging multimodal reasoning and knowledge distillation to improve note recommendation accuracy and interpretability in e-commerce.

Contribution

It proposes a new approach combining user interest modeling, multimodal reasoning, and knowledge distillation to generate more precise and interpretable tags for note recommendation.

Findings

Increases user engagement metrics in online tests.

Produces more fine-grained and relevant tags.

Enhances recommendation performance in cold-start scenarios.

Abstract

Large Language Models (LLMs) have shown promising potential in E-commerce community recommendation. While LLMs and Multimodal LLMs (MLLMs) are widely used to encode notes into implicit embeddings, leveraging their generative capabilities to represent notes with interpretable tags remains unexplored. In the field of tag generation, traditional close-ended methods heavily rely on the design of tag pools, while existing open-ended methods applied directly to note recommendations face two limitations: (1) MLLMs lack guidance during generation, resulting in redundant tags that fail to capture user interests; (2) The generated tags are often coarse and lack fine-grained representation of notes, interfering with downstream recommendations. To address these limitations, we propose TagLLM, a fine-grained tag generation method for note recommendation. TagLLM captures user interests across note categories through a User Interest Handbook and constructs fine-grained tag data using multimodal CoT Extraction. A Tag Knowledge Distillation method is developed to equip small models with competitive generation capabilities, enhancing inference efficiency. In online A/B test, TagLLM increases average view duration per user by 0.31%, average interactions per user by 0.96%, and page view click-through rate in cold-start scenario by 32.37%, demonstrating its effectiveness.