SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation

• URL: http://arxiv.org/abs/2509.21777v1
• Date: Fri, 26 Sep 2025 02:27:04 GMT
• Title: SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation
• Authors: Vianne R. Gao, Chen Xue, Marc Versage, Xie Zhou, Zhongruo Wang, Chao Li, Yeon Seonwoo, Nan Chen, Zhen Ge, Gourab Kundu, Weiqi Zhang, Tian Wang, Qingjun Cui, Trishul Chilimbi,
• Abstract summary: generative sequence models have shown promise in unifying retrieval and ranking by auto-regressively generating ranked items.<n>We introduce textitSynerGen, a novel generative recommender model that bridges this critical gap by providing a single generative backbone for both personalized search and recommendation.
• Score: 21.270980014269387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The dominant retrieve-then-rank pipeline in large-scale recommender systems suffers from mis-calibration and engineering overhead due to its architectural split and differing optimization objectives. While recent generative sequence models have shown promise in unifying retrieval and ranking by auto-regressively generating ranked items, existing solutions typically address either personalized search or query-free recommendation, often exhibiting performance trade-offs when attempting to unify both. We introduce \textit{SynerGen}, a novel generative recommender model that bridges this critical gap by providing a single generative backbone for both personalized search and recommendation, while simultaneously excelling at retrieval and ranking tasks. Trained on behavioral sequences, our decoder-only Transformer leverages joint optimization with InfoNCE for retrieval and a hybrid pointwise-pairwise loss for ranking, allowing semantic signals from search to improve recommendation and vice versa. We also propose a novel time-aware rotary positional embedding to effectively incorporate time information into the attention mechanism. \textit{SynerGen} achieves significant improvements on widely adopted recommendation and search benchmarks compared to strong generative recommender and joint search and recommendation baselines. This work demonstrates the viability of a single generative foundation model for industrial-scale unified information access.

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