15 citations · 50 across the 17 of their papers we have counts for
9 papers · 1 filter
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…