18 citations · 98 across the 76 of their papers we have counts for
23 papers · 1 filter
Collaborative Memory Augmentation for Generative Recommendation
Enze Liu, Zhen Tian, Wayne Xin Zhao
Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily f…
Dual-Stream MLP is All You Need for CTR Prediction
Kesha Ou, Zhen Tian, Wayne Xin Zhao +3
Click-through rate (CTR) prediction holds a pivotal role in online advertising and recommendation systems, where even small improvements can significantly boost revenue. Existing r…
Deep Research for Recommender Systems
Kesha Ou, Chenghao Wu, Xiaolei Wang +6
The technical foundations of recommender systems have progressed from collaborative filtering to complex neural models and, more recently, large language models. Despite these tech…
Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers
Bingqian Li, Bowen Zheng, Xiaolei Wang +5
Large language models (LLMs) have shown great promise in recommender systems, where supervised fine-tuning (SFT) is commonly used for adaptation. Subsequent studies further introdu…
GenCI: Generative Modeling of User Interest Shift via Cohort-based Intent Learning for CTR Prediction
Kesha Ou, Zhen Tian, Wayne Xin Zhao +2
Click-through rate (CTR) prediction plays a pivotal role in online advertising and recommender systems. Despite notable progress in modeling user preferences from historical behavi…
LARES: Latent Reasoning for Sequential Recommendation
Enze Liu, Bowen Zheng, Xiaolei Wang +4
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequ…
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation
Bowen Zheng, Xiaolei Wang, Enze Liu +5
Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation ba…
Universal Item Tokenization for Transferable Generative Recommendation
Bowen Zheng, Hongyu Lu, Yu Chen +2
Recently, generative recommendation has emerged as a promising paradigm, attracting significant research attention. The basic framework involves an item tokenizer, which represents…
Pre-training Generative Recommender with Multi-Identifier Item Tokenization
Bowen Zheng, Enze Liu, Zhongfu Chen +4
Generative recommendation autoregressively generates item identifiers to recommend potential items. Existing methods typically adopt a one-to-one mapping strategy, where each item…
Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Xiaolei Wang, Chunxuan Xia, Junyi Li +5
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies i…
Slow Thinking for Sequential Recommendation
Junjie Zhang, Beichen Zhang, Wenqi Sun +4
To develop effective sequential recommender systems, numerous methods have been proposed to model historical user behaviors. Despite the effectiveness, these methods share the same…
Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation
Enze Liu, Bowen Zheng, Wayne Xin Zhao +1
In recent years, substantial research efforts have been devoted to enhancing sequential recommender systems by integrating abundant side information with ID-based collaborative inf…
Self-Calibrated Listwise Reranking with Large Language Models
Ruiyang Ren, Yuhao Wang, Kun Zhou +5
Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passa…
EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention
Zhen Tian, Wayne Xin Zhao, Changwang Zhang +3
To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mech…
Sequence-level Semantic Representation Fusion for Recommender Systems
Lanling Xu, Zhen Tian, Bingqian Li +4
With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the u…
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Ruiyang Ren, Peng Qiu, Yingqi Qu +5
Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e…
Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis
Lanling Xu, Junjie Zhang, Bingqian Li +4
Recently, Large Language Models~(LLMs) such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender sys…
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential Recommendation
Wenqi Sun, Ruobing Xie, Junjie Zhang +3
Pre-trained recommendation models (PRMs) have received increasing interest recently. However, their intrinsically heterogeneous model structure, huge model size and computation cos…
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
Zhen Tian, Changwang Zhang, Wayne Xin Zhao +3
Click-Through Rate (CTR) prediction, which aims to estimate the probability of a user clicking on an item, is a key task in online advertising. Numerous existing CTR models concent…
Scaling Law of Large Sequential Recommendation Models
Gaowei Zhang, Yupeng Hou, Hongyu Lu +3
Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with mod…
Universal Multi-modal Multi-domain Pre-trained Recommendation
Wenqi Sun, Ruobing Xie, Shuqing Bian +2
There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems. However, Existing pre-trained multi-do…
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu +4
Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there…
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Junjie Zhang, Yupeng Hou, Ruobing Xie +5
Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies m…