338 citations · 1.4k across the 188 of their papers we have counts for
68 papers · 1 filter
Self-improving data synthesis enables scalable adaptation of embedding models
Zucan Lyu, Rui Tang, Jianlyu Chen +3
AI agents require specialised retrieval across domains and tasks, but adapting embedding models is constrained by costly manual training-data design. Here we introduce SYNTRA, a se…
Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval
Gaoming Zhang, Angqing Jiang, Jianchun Song +4
Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabilities for candidate items. Howe…
SWIM: Step-Wise Integrated Measure for Session-supervised List Evaluation in Generative Re-ranking
Yuanhao Pu, Chenghao Zhang, Chao Feng +6
Modern industrial recommender systems have increasingly adopted the Generator-Evaluator (G-E) framework for the re-ranking stage. Within this paradigm, the generator produces candi…
Think-to-Personalize: Unifying Reasoning and Retrieval for User-Centric Personalized Dense Retrieval
Angqing Jiang, Gaoming Zhang, Jianchun Song +4
Dense retrieval has become a cornerstone of modern local-lifestyle e-commerce search by encoding queries and items into semantic embedding spaces. While recent advancements have tr…
DIRECTOR: Dynamic Index-based Recommendation with Transport-Optimized Retrieval
Yuanhao Pu, Chenghao Zhang, Chao Feng +2
Reranking is a combinatorial decision problem that aims to select and order a high-utility slate from a request-specific candidate set. A major line of generative rerankers adopts…
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…
Similar Users-Augmented Interest Network
Xiaolong Chen, Haoyi Zhao, Xu Huang +1
Click-through rate (CTR) prediction is one of the core tasks in recommender systems. User behavior sequences, as one of the most effective features, can accurately reflect user pre…
Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders
Angqing Jiang, Jianlyu Chen, Zhe Fang +4
Effective medical text retrieval requires both high accuracy and low latency. While LLM-based embedding models possess powerful retrieval capabilities, their prohibitive latency an…
FuXi-Linear: Unleashing the Power of Linear Attention in Long-term Time-aware Sequential Recommendation
Yufei Ye, Wei Guo, Hao Wang +7
Modern recommendation systems primarily rely on attention mechanisms with quadratic complexity, which limits their ability to handle long user sequences and slows down inference. W…
Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
Luankang Zhang, Hao Wang, Zhongzhou Liu +8
The scarcity of high-quality training data presents a fundamental bottleneck to scaling machine learning models. This challenge is particularly acute in recommendation systems, whe…
From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models
Mingjia Yin, Junwei Pan, Hao Wang +5
Click-Through Rate (CTR) prediction, a core task in recommendation systems, aims to estimate the probability of users clicking on items. Existing models predominantly follow a disc…
OpenOneRec Technical Report
Guorui Zhou, Honghui Bao, Jiaming Huang +44
While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation sy…
Retro*: Optimizing LLMs for Reasoning-Intensive Document Retrieval
Junwei Lan, Jianlyu Chen, Zheng Liu +3
With the growing popularity of LLM agents and RAG, it has become increasingly important to retrieve documents that are essential for solving a task, even when their connection to t…
ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval
Jianlyu Chen, Junwei Lan, Chaofan Li +2
In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. Fir…
MR-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval
Junjie Zhou, Ze Liu, Lei Xiong +10
Multimodal retrieval is becoming a crucial component of modern AI applications, yet its evaluation lags behind the demands of more realistic and challenging scenarios. Existing ben…
FuXi-β: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model
Yufei Ye, Wei Guo, Hao Wang +7
Scaling laws for autoregressive generative recommenders reveal potential for larger, more versatile systems but mean greater latency and training costs. To accelerate training and…
DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction
Kefan Wang, Hao Wang, Wei Guo +4
Click-through rate (CTR) prediction is a critical task in online advertising and recommender systems, relying on effective modeling of feature interactions. Explicit interactions c…
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis
Chaofan Li, Jianlyu Chen, Yingxia Shao +2
Code embedding models attract increasing attention due to the widespread popularity of retrieval-augmented generation (RAG) in software development. These models are expected to ca…
Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model
Luankang Zhang, Kenan Song, Yi Quan Lee +7
In recommendation systems, the traditional multi-stage paradigm, which includes retrieval and ranking, often suffers from information loss between stages and diminishes performance…
A Universal Framework for Compressing Embeddings in CTR Prediction
Kefan Wang, Hao Wang, Kenan Song +6
Accurate click-through rate (CTR) prediction is vital for online advertising and recommendation systems. Recent deep learning advancements have improved the ability to capture feat…
Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
Hao Wang, Wei Guo, Luankang Zhang +7
In the era of information overload, recommendation systems play a pivotal role in filtering data and delivering personalized content. Recent advancements in feature interaction and…
FuXi-: Scaling Recommendation Model with Feature Interaction Enhanced Transformer
Yufei Ye, Wei Guo, Jin Yao Chin +8
Inspired by scaling laws and large language models, research on large-scale recommendation models has gained significant attention. Recent advancements have shown that expanding se…
HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks
Hongjin Qian, Zheng Liu, Chao Gao +3
In real-world information-seeking scenarios, users have dynamic and diverse needs, requiring RAG systems to demonstrate adaptable resilience. To comprehensively evaluate the resili…
AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark
Jianlyu Chen, Nan Wang, Chaofan Li +6
Evaluation plays a crucial role in the advancement of information retrieval (IR) models. However, current benchmarks, which are based on predefined domains and human-labeled data,…