Generative Enhanced Modeling: A Collaborative Framework for Enhancing User Representations via Semantic ID
TL;DR
GEM shifts the paradigm from self-behavior induction to collective experience migration to collective experience migration and constructs LLM-based semantic IDs and embeddings and effectively suppresses reward-driven noise while preserving target-aware signals.
Abstract
User interest modeling is foundational to recommender systems. However, sparse and noisy behaviors make traditional item-level sequence models brittle, especially for new and low-activity users. Furthermore, relying solely on a user's own history limits exploration and reinforces the ''filter bubbles''. To address this, we propose GEM (Generative Enhanced Modeling). GEM shifts the paradigm from self-behavior induction to collective experience migration. Specifically, it constructs LLM-based semantic IDs and embeddings. Grounded in information theory, GEM performs multi-stage denoising at both the user and item levels. This design effectively suppresses reward-driven noise while preserving target-aware signals. We deployed GEM on the Alipay Tab3 video feed. Offline evaluations show significant GAUC gains. Online A/B tests demonstrate a 0.9% lift in watch time alongside stable video views and improved exposure diversity. These results confirm that GEM enhances recommendation quality and successfully broadens user interests.