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This paper introduces TAGR, a generative recommendation framework designed to address the dynamic challenges of live-streaming advertising by implementing temporal adaptation at three critical levels: live-ad tokenization, user intent modeling, and preference alignment. By utilizing Live Semantic-Collaborative IDs to refresh ad representations and Intent-Aware Generation to model user behavior, TAGR effectively captures evolving user intent and optimizes preferences in real-time. The framework was deployed on a large-scale e-commerce platform, resulting in an 8.5% increase in live-room entry rates, a 7.4% rise in shopping-cart clicks, and a substantial 16.1% revenue boost over existing systems.
TAGR's innovative approach to real-time user intent modeling and ad tokenization leads to significant revenue gains in live-stream advertising.
Live-streaming advertising is an important monetization channel on short-video and e-commerce platforms, where rapidly changing live content, promoted products, and user feedback impose strong freshness requirements on recommendation models. Existing generative recommenders designed for static domains fail at three levels: static semantic IDs (SID) cannot track evolving live ads; single-scale behavior modeling misses shifting intent; preference optimization conflicts between fresh on-policy feedback and training stability. We propose TAGR, a generative recommendation framework with temporal adaptation at three levels: live-ad tokenization, user intent modeling, and preference alignment. At the token level, Live Semantic-Collaborative ID (LSID) periodically refreshes each active ad's SID based on its current live scene and promoted products, while retaining a stable hierarchical token vocabulary for autoregressive generation. At the intent level, Intent-Aware Generation (IAG) models live-room entry histories at multiple temporal granularities as the primary intent sequence, keeps auxiliary behaviors as separate inputs, and weights next-token prediction (NTP) using post-request intent evidence and business value. At the alignment level, Intermittent On-Policy Preference Optimization (IOPO) periodically samples fresh candidate groups from the current policy and performs behavior- and value-aligned preference updates interleaved with supervised NTP maintenance to preserve learned behavior distribution. Deployed on a large-scale e-commerce live-stream advertising platform, TAGR improves live-room entry and shopping-cart click rates by 8.5% and 7.4%, respectively, and achieves a 16.1% revenue lift over the production baseline. These results demonstrate the effectiveness and industrial viability of temporally adaptive generative recommendation for live-stream advertising.
