15 citations · 50 across the 17 of their papers we have counts for
18 papers
Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction
Zhicheng Zhang, Zhaocheng Du, Jieming Zhu +8
User behavior sequences in modern recommendation systems exhibit significant length heterogeneity, ranging from sparse short-term interactions to rich long-term histories. While lo…
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…
MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
Zhipeng Bian, Jieming Zhu, Xuyang Xie +3
The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices.…
TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
Xianquan Wang, Zhaocheng Du, Jieming Zhu +3
Feature interaction modeling is crucial for deep recommendation models. A common and effective approach is to construct explicit feature combinations to enhance model performance.…
Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
Yifan Wang, Weinan Gan, Longtao Xiao +7
Generative recommendation (GR) typically encodes behavioral or semantic aspects of item information into discrete tokens, leveraging the standard autoregressive (AR) generation par…
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Minjie Hong, Yan Xia, Zehan Wang +8
Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their e…
A Survey of Personalized Large Language Models: Progress and Future Directions
Jiahong Liu, Zexuan Qiu, Zhongyang Li +7
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing sty…
UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration
Longtao Xiao, Haozhao Wang, Cheng Wang +6
With the rise of generative paradigms, generative recommendation has garnered increasing attention. The core component is the item code, generally derived by quantizing collaborati…
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Zeyu Zhang, Quanyu Dai, Luyu Chen +7
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries. However, there still lack…
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering
Weixi Weng, Jieming Zhu, Xiaojun Meng +3
Multimodal large language models (MLLMs) have demonstrated great performance on visual question answering (VQA). When it comes to knowledge-based Visual Question Answering (KB-VQA)…
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…
Multimodal Pretraining and Generation for Recommendation: A Tutorial
Jieming Zhu, Chuhan Wu, Rui Zhang +1
Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models pr…
A Survey on the Memory Mechanism of Large Language Model based Agents
Zeyu Zhang, Xiaohe Bo, Chen Ma +6
Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are feature…
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…
PMG : Personalized Multimodal Generation with Large Language Models
Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao +2
The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both th…
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Yushen Li, Jinpeng Wang, Tao Dai +4
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies pre…
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
Qijiong Liu, Jieming Zhu, Yanting Yang +6
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely…
Ten Challenges in Industrial Recommender Systems
Zhenhua Dong, Jieming Zhu, Weiwen Liu +1
Huawei's vision and mission is to build a fully connected intelligent world. Since 2013, Huawei Noah's Ark Lab has helped many products build recommender systems and search engines…