GLASS: A Generative Recommender for Long-sequence Modeling via SID-Tier and Semantic Search

• URL: http://arxiv.org/abs/2602.05663v1
• Date: Thu, 05 Feb 2026 13:48:33 GMT
• Title: GLASS: A Generative Recommender for Long-sequence Modeling via SID-Tier and Semantic Search
• Authors: Shiteng Cao, Junda She, Ji Liu, Bin Zeng, Chengcheng Guo, Kuo Cai, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Zhiheng Li, Cheng Yang,
• Abstract summary: GLASS is a novel framework that integrates long-term user interests into the generative process via SID-Tier and Semantic Search.<n>We show that GLASS outperforms state-of-the-art baselines in experiments on two large-scale real-world datasets.
• Score: 51.44490997013772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. While generative recommender systems have emerged as a transformative paradigm, they face hurdles in effectively modeling extensive historical sequences. To address this challenge, we propose GLASS, a novel framework that integrates long-term user interests into the generative process via SID-Tier and Semantic Search. We first introduce SID-Tier, a module that maps long-term interactions into a unified interest vector to enhance the prediction of the initial SID token. Unlike traditional retrieval models that struggle with massive item spaces, 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, 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. To address the inherent data sparsity in semantic hard search, we propose two strategies: semantic neighbor augmentation and codebook resizing. Extensive experiments on two large-scale real-world datasets, TAOBAO-MM and KuaiRec, demonstrate that GLASS outperforms state-of-the-art baselines, achieving significant gains in recommendation quality. Our codes are made publicly available to facilitate further research in generative recommendation.

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