18 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…
KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation
Jiangxia Cao, Hao Peng, Wenlong Xu +97
Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recom…
TAGR: Temporally Adaptive Generative Recommendation for Industrial Live-Streaming Advertising
Wencai Ye, Guangyi Liu, Chaoyi Wang +7
Live-streaming advertising is an important monetization channel on short-video and e-commerce platforms, where rapidly changing live content, promoted products, and user feedback i…
From a Static Multi-Level Small Semantic Codebook to a Dynamic Single-Level Large Semantic Codebook for Generative Recommendation
Tianlu Xie, Xin Ku, Mingjie Sun +8
Generative recommendation represents each item with a sequence of discrete Semantic IDs (SIDs) and predicts the sequence to retrieve the next item. Typical systems use multi-level…
RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems
Haoran Ling, Yuecheng Li, Zeyu Song +5
Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based agents can…
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…
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…
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
Yuecheng Li, Zeyu Song, Jing Yao +3
Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID spac…
Reinforced Preference Optimization for Reasoning-Augmented Recommendations
Jingtong Gao, Zeyu Song, Chi Lu +7
Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…
Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation
Jiaju Chen, Chongming Gao, Chenxiao Fan +4
Large language model (LLM)-based generative list-wise recommendation has advanced rapidly, but decoding remains sequential and thus latency-prone. To accelerate inference without c…
CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
Lixiang Wang, Shaoyun Shi, Peng Wang +2
To balance effectiveness and efficiency in recommender systems, multi-stage pipelines employ lightweight two-tower models for large-scale candidate retrieval. However, their isolat…
CS3: Efficient Online Capability Synergy for Two-Tower Recommendation
Lixiang Wang, Shaoyun Shi, Peng Wang +2
To balance effectiveness and efficiency in recommender systems, multi-stage pipelines commonly use lightweight two-tower models for large-scale candidate retrieval. However, the is…
Generative Recommendation for Large-Scale Advertising
Ben Xue, Dan Liu, Lixiang Wang +27
Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generat…
RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems
Runpeng Cui, Zhipeng Sun, Chi Lu +1
Predicting continuous values such as watch-time and gross merchandise value (GMV) is a core problem in industrial recommendation systems. Its inherent difficulty stems from the hig…
RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment
Yuecheng Li, Hengwei Ju, Zeyu Song +4
Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic hetero…
Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
Wei Yang, Rui Zhong, Yiqun Chen +2
Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noi…
TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
Yu Xia, Rui Zhong, Zeyu Song +5
The extensive world knowledge and powerful reasoning capabilities of large language models (LLMs) have attracted significant attention in recommendation systems (RS). Specifically,…
R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
Hao Gu, Rui Zhong, Yu Xia +4
Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily…