338 citations · 454 across the 70 of their papers we have counts for
28 papers · 1 filter
Rethinking Item Tokenization in Generative Recommenders: From Fixed Atoms to Semantic Subwords
Xinrui Miao, Mingjia Yin, Jiaqing Zhang +5
In generative recommender systems, items are typically tokenized into fixed-length semantic ID sequences for autoregressive next-item prediction. However, for user-context modeling…
SITA: Semantic Interest Tokens for Target-Aware Compression in Long-Sequence Recommendation
Rui Zhou, Bo Chen, Qinglin Jia +5
As user behavior histories continue to grow on modern Internet platforms, effectively modeling long behavior sequences has become crucial for predicting user interests in candidate…
DIET: Learning to Distill Dataset Continually for Recommender Systems
Jiaqing Zhang, Hao Wang, Mingjia Yin +6
Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, ret…
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
Kai Cheng, Hao Wang, Wei Guo +4
Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked l…
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…
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…
RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering
Rongyang Zhang, Yuqing Huang, Chengqiang Lu +8
In real-world scenarios, providing user queries with visually enhanced responses can considerably benefit understanding and memory, underscoring the great value of interleaved imag…
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…
Enhancing CTR Prediction with De-correlated Expert Networks
Jiancheng Wang, Mingjia Yin, Hao Wang +1
Modeling feature interactions is essential for accurate click-through rate (CTR) prediction in advertising systems. Recent studies have adopted the Mixture-of-Experts (MoE) approac…
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…
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
Jiaqing Zhang, Mingjia Yin, Hao Wang +5
In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on l…
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…
Scaling New Frontiers: Insights into Large Recommendation Models
Wei Guo, Hao Wang, Luankang Zhang +16
Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate inc…
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
Xiang Xu, Hao Wang, Wei Guo +6
Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the…
MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation
Junxiong Tong, Mingjia Yin, Hao Wang +3
Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance, especially in sparse data or new user scenarios. However, CDR faces challenges s…
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Hao Wang, Yongqiang Han, Kefan Wang +6
In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit sema…
Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method
Ze Liu, Jin Zhang, Chao Feng +3
Although advancements in deep learning have significantly enhanced the recommendation accuracy of deep recommendation models, these methods still suffer from low recommendation eff…
Dataset Regeneration for Sequential Recommendation
Mingjia Yin, Hao Wang, Wei Guo +5
The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been…
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation
Mingjia Yin, Hao Wang, Wei Guo +6
Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains. While significant endeavors have be…
A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation
Luankang Zhang, Hao Wang, Suojuan Zhang +5
Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start…
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
Yongqiang Han, Hao Wang, Kefan Wang +6
In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user…