Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2504.01304v1)

Abstract: The integration of LLMs with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits scalability in large-scale corpora. In this paper, we propose the Real-time Ad REtrieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time. These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance. Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs. RARE has been implemented in a real-world online system, handling daily search volumes in the hundreds of millions. The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions. Extensive offline experiments show RARE's superiority over ten competitive baselines in four major categories.

Real-time Ad Retrieval via LLM-Generated Commercial Intention for Sponsored Search Advertising

The paper "Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising," authored by researchers from Tencent Inc., presents a novel framework designed to enhance ad retrieval processes in large-scale online systems. The framework, known as Real-time Ad REtrieval (RARE), employs customized LLMs to generate Commercial Intentions (CIs) as semantic intermediaries, facilitating efficient and scalable ad retrieval for user queries.

Problem Context

Traditional ad retrieval systems typically engage in a two-stage process, utilizing manually selected keywords to fetch ads. This approach, while functional, imposes significant inefficiencies and recall issues due to the gap between the user's queries and the keyword choices. Moreover, methods utilizing heavy DocIDs face challenges concerning real-time generation and scalability, particularly in handling vast ad libraries and dynamically updating candidate sets.

Proposed Framework

RARE addresses these inefficiencies by replacing keyword or DocID-based indexing with LLM-generated CIs. These CIs encapsulate the commercial intentions associated with ads and queries, allowing for direct and real-time retrieval of advertisements. The framework employs a two-pronged LLM strategy: the offline phase generates a corpus of CIs for ads, while the online phase facilitates real-time CI generation for user queries using constrained beam search.

Key Innovations

1. Customized LLM Development: The authors enhance the base LLM through knowledge injection and format fine-tuning, incorporating domain-specific commercial knowledge. This enables the LLM to generate relevant CIs, improving semantic retrieval efficiency and extension.
2. Constrained Beam Search: This decoding process ensures that meanings are constrained to the CI set, optimizing both the diversity and relevance of generated ads while maintaining decoding efficiency.
3. Dynamic Indexing: RARE constructs a dynamic ad index, mapping CIs to ads in a one-to-many relationship, enabling a scalable and versatile retrieval model suitable for real-time environments.

Empirical Evaluation

RARE's implementation spans real-world applications with high query volumes, exceeding hundreds of millions per day. The paper reports quantitative improvements in operations using RARE, specifically a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR), and a 5.29% increase in shallow conversions. The framework outperformed ten baseline methods across various metrics, including HR@500, MAP, and ACR.

Implications and Future Directions

The deployment of RARE in practical environments underscores its potential to transform ad retrieval mechanisms by enhancing both semantic alignment and system efficiency. This framework's embrace of LLMs in ad retrieval sets a precedent for exploring further integration of LLMs in commercial applications. Additionally, this method highlights the growing viability of generative models in real-time ad systems beyond traditional retrieval processes.

Future developments in this area might include refining generative capabilities to incorporate real-time semantic feedback and context-aware modifications in commercial intents. Additionally, exploring hybrid models that juxtapose generative and discriminative predictions could enhance alignment accuracy, thus expanding the application scope of sequence-generated systems in commercial technology platforms.

In summary, RARE represents a significant step in optimizing sponsored search systems through innovative use of LLMs, proving its merits both theoretically and practically in extensive online frameworks.