Coarse-to-Fine Long-term Interest Modeling for Generative Recommendation

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. Due to the quadratic complexity of attention mechanisms, existing GR models are typically confined to short interaction sequences. While pioneer works have attempted to adapt Search-based Interest Models (SIM) to the generative context, they typically overlook the inherent hierarchical distinction of SIDs. GR is fundamentally a coarse-to-fine generation task, where the initial SIDs (prefix) determine the broad semantic category and the subsequent SIDs (suffix) pinpoint the specific item. Thus, our core insight is that the prefix and suffix of SIDs require distinct long-term signal injections. To bridge this gap, we propose GLASS, a Generative recommendation framework that integrates Long-term user interests into the generative process viA SIDTier and Semantic Search. For the generation of SID prefix, we introduce SID-Tier, a module that maps long-term interactions into a unified interest vector to enhance the prediction of the initial SID token. SID-Tier leverages the compact nature of the semantic codebook to incorporate cross features between the user’s long-term history and candidate semantic codes. Furthermore, for the generation of SID suffix, we present semantic hard search, which utilizes generated coarse-grained semantic ID as dynamic keys to extract relevant historical behaviors, which are then fused via an adaptive gated fusion module to recalibrate the trajectory of subsequent fine-grained tokens. Extensive experiments on two large-scale real-world datasets, TAOBAO-MM and KuaiRec, demonstrate that GLASS outperforms state-of-the-art baselines. A two-week online A/B test on a short-video platform demonstrate that GLASS achieves significant gains in recommendation quality. Our codes are publicly available at this anonymous link to facilitate further research in generative recommendation.