Integrates auction bids and monetization logic directly into generative recommender systems (like TIGER) via bid-aware decoding.
March 24, 2026
Original Paper
One Model, Two Markets: Bid-Aware Generative Recommendation
arXiv · 2603.22231
The Takeaway
Generative RecSys has struggled with industry adoption because it lacks clear mechanisms for ad placement and pricing. GEM-Rec enables real-time monetization without retraining, guaranteeing that higher bids increase an item's likelihood of being generated.
From the abstract
Generative Recommender Systems using semantic ids, such as TIGER (Rajput et al., 2023), have emerged as a widely adopted competitive paradigm in sequential recommendation. However, existing architectures are designed solely for semantic retrieval and do not address concerns such as monetization via ad revenue and incorporation of bids for commercial retrieval. We propose GEM-Rec, a unified framework that integrates commercial relevance and monetization objectives directly into the generative seq