1 citations · 1 across the 14 of their papers we have counts for
17 papers
OneLatent: Latent Reasoning for Efficient Foundation Recommendation Models
Hao Jiang, Peiru Du, Pengfei Yao +10
Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their use as the backbone of foundation recommendation models (FRMs). Existing methods enha…
Advancing Model Research in AgentX: Long-Horizon Autonomy for Industrial Recommender Systems
Shuang Yang, Zijie Zhuang, Changxin Lao +20
Sustaining industrial recommendation research requires using the results of one experiment to decide what to investigate next. We present AgentX-Model, the next generation of Agent…
HubMixer: Progressive Latent Hub Mixing for Parameter-Efficient Feature Interaction in Recommendation
Jie Zhou, Zixian Gong, Wenhao Li +6
Learning effective feature interactions is central to industrial recommendation and advertising ranking systems. Recent token-mixing architectures simplify self-attention with ligh…
EvoReason: Self-Evolving Reasoning Primitive-Guided On-Policy Distillation for Latent Reasoning in Generative Recommendation
Zhuang Zhuang, Zhipeng Wei, Rongfeng Guo +4
Generative recommendation benefits from reasoning-enhanced inference, and latent reasoning offers an efficient paradigm by encoding intermediate reasoning processes into compact co…
Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation
Long Zhang, Hao Jiang, Sheng Yu +3
While large language models (LLMs) have advanced ID-based recommendation through Semantic ID (SID) modeling, existing SID generation frameworks largely follow a single-representati…
From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation
Zhi Chen, Minmao Wang, Xingchen Liu +8
Semantic-ID-based generative recommenders enable efficient next-item generation, but their item-level supervision mainly captures behavioral co-occurrence and local transitions. La…
WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models
Hao Jiang, Peiru Du, Pengfei Yao +10
Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs). Existing approache…
OneReason Technical Report
OneRec Team, Biao Yang, Boyang Ding +81
Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…
SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks
Zhuang Zhuang, Zhipeng Wei, Ji Dai +4
Linear attention provides an efficient backbone for long-sequence recommendation by avoiding the quadratic cost of standard Transformers, but its compressed recurrent state can be…
LBM: Hierarchical Large Auto-Bidding Model via Reasoning and Acting
Yewen Li, Zhiyi Lyu, Peng Jiang +3
The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers a…
ReST: A Plug-and-Play Spatially-Constrained Representation Enhancement Framework for Local-Life Recommendation
Hao Jiang, Long Zhang, Guoquan Wang +6
Local-life recommendation have witnessed rapid growth, providing users with convenient access to daily essentials. However, this domain faces two key challenges: (1) spatial constr…
Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Minmao Wang, Xingchen Liu, Shijie Yi +5
Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcemen…
Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner
Yewen Li, Jingtong Gao, Nan Jiang +7
Auto-bidding is central to computational advertising, achieving notable commercial success by optimizing advertisers' bids within economic constraints. Recently, large generative m…
Generative Auto-Bidding with Value-Guided Explorations
Jingtong Gao, Yewen Li, Shuai Mao +8
Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms.…
GAS: Generative Auto-bidding with Post-training Search
Yewen Li, Shuai Mao, Jingtong Gao +6
Auto-bidding is essential in facilitating online advertising by automatically placing bids on behalf of advertisers. Generative auto-bidding, which generates bids based on an adjus…
ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising
Ruizhi Wang, Yu Rong, Kai Liu +4
In digital advertising, demand-side platforms (DSPs) allow advertisers to create multiple ad creatives from a single photo for real-time bidding. While increasing the number of cre…
LDACP: Long-Delayed Ad Conversions Prediction Model for Bidding Strategy
Peng Cui, Yiming Yang, Fusheng Jin +8
In online advertising, once an ad campaign is deployed, the automated bidding system dynamically adjusts the bidding strategy to optimize Cost Per Action (CPA) based on the number…