Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models

2026-06-17

AI Summary

This work addresses the challenge of efficiently integrating traditional heterogeneous signals into large-scale Transformer-based recommendation models, which often suffer from excessively long prompts, high memory consumption, and substantial computational overhead. To overcome these limitations, the authors propose Token Factory, a novel framework that introduces a “soft token” mechanism. This approach compresses multi-source heterogeneous features and encodes them into compact, Transformer-compatible representations, enabling direct input to large models without causing prompt length explosion. Evaluated in industrial-scale recommendation scenarios, the method significantly reduces both memory footprint and computational cost while consistently improving recommendation performance.

Abstract

Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically integrating traditional signals into these transformer-based architectures effectively and efficiently remains a major challenge. Conventional approaches that "textualize" these signals directly or create discrete item representations often lead to excessively long prompts, substantial memory footprints, and high computational overhead. To overcome these limitations, we propose "Token Factory", a framework designed to transform traditional signals into "soft tokens" that can be directly processed by LRMs. This approach enables efficient integration and compression of heterogeneous input features, preventing prompt length explosion while enhancing model performance. We detail the architecture of Token Factory and present experimental results validating its effectiveness in a production-scale recommendation environment.

Problem

• Large Recommendation Models
• heterogeneous signals
• efficient integration
• prompt length explosion
• computational overhead

Innovation

• Token Factory
• soft tokens
• Large Recommendation Models
• heterogeneous signals
• efficient integration