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arXiv · 2511.12518

DualGR: Generative Retrieval with Long and Short-Term Interests Modeling

Zhongchao Yi·Kai Feng·Xiaojian Ma·Yalong Wang·Yongqi Liu·Han Li·Zhengyang Zhou·Yang Wang
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Abstract

In large-scale industrial recommendation systems, retrieval must produce high-quality candidates from massive corpora under strict latency. Recently, Generative Retrieval (GR) has emerged as a viable alternative to Embedding-Based Retrieval (EBR), which quantizes items into a finite token space and decodes candidates autoregressively, providing a scalable path that explicitly models target-history interactions via cross-attention. However, deploying GR in short-video feeds remains challenged by long-short interest interference, context-induced noise in hierarchical SID generation, and the lack of explicit learning from exposed-but-unclicked feedback. To address these challenges, we propose DualGR, which combines (i) a Dual-Branch Long/Short-Term Router (DBR) with selective activation, (ii) Search-based SID Decoding (S2D) that constrains fine-level decoding within the current coarse bucket for efficiency and noise control, and (iii) an Exposure-aware Next-Token Prediction Loss (ENTP-Loss) that treats unclicked exposures as coarse-level hard negatives to promote timely interest fade-out. On the large-scale Kuaishou short-video recommendation system, DualGR has achieved outstanding performance. Online A/B testing shows +0.527% video views and +0.432% watch time lifts, validating DualGR as a practical and effective paradigm for industrial generative retrieval.

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Zhongchao Yi, Kai Feng, Xiaojian Ma, Yalong Wang, Yongqi Liu, Han Li, Zhengyang Zhou, Yang Wang. 2025-11-16. DualGR: Generative Retrieval with Long and Short-Term Interests Modeling. https://arxiv.org/abs/2511.12518

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