One Model, Two Markets: Bid-Aware Generative Recommendation

ArXi:2603.22231v1 Announce Type: cross Generative Recommender Systems using semantic ids, such as TIGER (Rajput, 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 sequence. We