SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909807082995712 |
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| author | Gao, Vianne R. Xue, Chen Versage, Marc Zhou, Xie Wang, Zhongruo Li, Chao Seonwoo, Yeon Chen, Nan Ge, Zhen Kundu, Gourab Zhang, Weiqi Wang, Tian Cui, Qingjun Chilimbi, Trishul |
| author_facet | Gao, Vianne R. Xue, Chen Versage, Marc Zhou, Xie Wang, Zhongruo Li, Chao Seonwoo, Yeon Chen, Nan Ge, Zhen Kundu, Gourab Zhang, Weiqi Wang, Tian Cui, Qingjun Chilimbi, Trishul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation Gao, Vianne R. Xue, Chen Versage, Marc Zhou, Xie Wang, Zhongruo Li, Chao Seonwoo, Yeon Chen, Nan Ge, Zhen Kundu, Gourab Zhang, Weiqi Wang, Tian Cui, Qingjun Chilimbi, Trishul Computation and Language 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. |
| title | SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.21777 |