LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System

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Abstract

Display advertising plays a crucial role in benefiting advertisers, publishers, and users. Traditional display advertising systems employ a multi-stage architecture comprising retrieval, coarse ranking, ranking, and re-ranking. However, conventional retrieval methods primarily rely on ID-based learning-to-rank mechanisms, often underutilizing the content information of ads, like ads’ title, and description. This limitation reduces the ability to generate diverse and relevant recommendation lists. To address this challenge, we propose leveraging the extensive world knowledge of large language models (LLMs). However, effectively integrating LLMs into advertising systems presents three key challenges: (i) How to accurately capture user interests , (ii) How to bridge the knowledge gap between LLMs and advertising systems , and (iii) How to efficiently deploy LLMs at scale . To overcome these challenges, we introduce LEADRE —the L LM E mpowered Display AD vertisement RE commender system. LEADRE consists of three core components. The Intent-Aware Prompt Engineering module introduces multi-faceted knowledge and constructs intentaware <Prompt, Response> pairs, fine-tuning LLMs to generate ads tailored to users’ personal interests. The Advertising-Specific Knowledge Alignment module incorporates auxiliary fine-tuning tasks and Direct Preference Optimization (DPO) to align LLMs with advertising semantics and business objectives. The Latency-Aware Model Deployment module integrates a hybrid service framework that balances latency-tolerant and latency-sensitive service, ensuring seamless online deployment. Extensive offline experiments validate the effectiveness of LEADRE, demonstrating significant improvements across multiple evaluation metrics. Furthermore, online A/B tests reveal a 1.57% and 1.17% ∗ Work was done while Fengxin Li was intern at Tencent. † Corresponding authors. increase in Gross Merchandise Value (GMV) for serviced users on WeChat Channels and Moments, respectively. LEADRE has been successfully deployed on both platforms, handling tens of billions of requests daily.