22 citations · 42 across the 19 of their papers we have counts for
20 papers
Dual Exam: Controlled Evaluation of Multimodal Agents and World Models
Zhefan Rao, Meng Chu, Xuan Zhang +80
World Models (WMs) can support Multimodal Agents (MAs) through simulated experience, while Agent trajectories can inform World Model refinement. Evaluating this interaction require…
Effective Knowledge Transfer for Multi-Task Recommendation Models
Guohao Cai, Jun Yuan, Zhenhua Dong
The conversion rate (CVR) is a crucial metric for evaluating the effectiveness of platforms, as it quantifies the alignment of content with audience preferences. However, the limit…
RoTE: Coarse-to-Fine Multi-Level Rotary Time Embedding for Sequential Recommendation
Haolin Zhang, Longtao Xiao, Guohao Cai +2
Sequential recommendation models have been widely adopted for modeling user behavior. Existing approaches typically construct user interaction sequences by sorting items according…
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment
Zhipeng Bian, Jieming Zhu, Qijiong Liu +6
Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new possibilities for AI-generated content. Yet, personalized cover image generat…
FAIR: Focused Attention Is All You Need for Generative Recommendation
Longtao Xiao, Haolin Zhang, Guohao Cai +6
Recently, transformer-based generative recommendation has garnered significant attention for user behavior modeling. However, it often requires discretizing items into multi-code r…
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Xueyang Feng, Jingsen Zhang, Jiakai Tang +6
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generat…
A Contextual-Aware Position Encoding for Sequential Recommendation
Jun Yuan, Guohao Cai, Zhenhua Dong
Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendatio…
ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents
Jie-Jing Shao, Bo-Wen Zhang, Xiao-Wen Yang +8
Travel planning stands out among real-world applications of \emph{Language Agents} because it couples significant practical demand with a rigorous constraint-satisfaction challenge…
A Parameter Update Balancing Algorithm for Multi-task Ranking Models in Recommendation Systems
Jun Yuan, Guohao Cai, Zhenhua Dong
Multi-task ranking models have become essential for modern real-world recommendation systems. While most recommendation researches focus on designing sophisticated models for speci…
Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
Haiyuan Zhao, Guohao Cai, Jieming Zhu +3
In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time. However, watch time prediction suffers from du…
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
Junjie Huang, Guohao Cai, Jieming Zhu +4
Click-through rate (CTR) prediction plays an indispensable role in online platforms. Numerous models have been proposed to capture users' shifting preferences by leveraging user be…
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
Haiyuan Zhao, Lei Zhang, Jun Xu +3
In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but als…
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
Jieming Zhu, Guohao Cai, Junjie Huang +3
Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackl…
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
Kelong Mao, Jieming Zhu, Liangcai Su +3
Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many…
BARS: Towards Open Benchmarking for Recommender Systems
Jieming Zhu, Quanyu Dai, Liangcai Su +5
The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite significant progress made in both research and practice of recommender…
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Guohao Cai, Jieming Zhu, Quanyu Dai +4
Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users…
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
Haiyuan Zhao, Jun Xu, Xiao Zhang +3
Unbiased learning to rank has been proposed to alleviate the biases in the search ranking, making it possible to train ranking models with user interaction data. In real applicatio…
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Yi Li, Jieming Zhu, Weiwen Liu +6
The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has receive…
Debiased Recommendation with User Feature Balancing
Mengyue Yang, Guohao Cai, Furui Liu +5
Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS…
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
Chang Liu, Xiaoguang Li, Guohao Cai +3
Sequential recommender systems aim to model users' evolving interests from their historical behaviors, and hence make customized time-relevant recommendations. Compared with tradit…