7 citations · 13 across the 10 of their papers we have counts for
12 papers
ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents
Haohao Qu, Yongcheng Jing, Chun Hin Chan +3
Recent Recommendation Agents (RecAgents) offer a promising alternative by shifting recommendation to an active, user-side paradigm, where generative agents autonomously perceive ex…
AI Smart Glasses for Wearable Intelligence: From Egocentric Sensing to Agentic Personalization
Xu Yuan, Yi Wang, Zhuohang Jiang +8
Recent advances in artificial intelligence (AI) are reshaping smart glasses from egocentric capture and display devices into platforms for wearable intelligence. Smart glasses incr…
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
Zhuohang Jiang, Yuxin Chen, Shijie Wang +6
Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent o…
SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses
Zhuohang Jiang, Xu Yuan, Haohao Qu +4
The rapid advancement of AI-powered smart glasses-one of the hottest wearable devices-has unlocked new frontiers for multimodal interaction, with Visual Question Answering (VQA) ov…
DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis
Rui An, Haohao Qu, Wenqi Fan +2
Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications. Within this realm, Transformers have emerged a…
HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
Yi Zhou, Haohao Qu, Yunqing Liu +3
Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitfu…
Diffusion Generative Recommendation with Continuous Tokens
Haohao Qu, Shanru Lin, Yujuan Ding +2
Recent advances in generative artificial intelligence, particularly large language models (LLMs), have opened new opportunities for enhancing recommender systems (RecSys). Most exi…
A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models
Liangbo Ning, Ziran Liang, Zhuohang Jiang +8
With the advancement of web techniques, they have significantly revolutionized various aspects of people's lives. Despite the importance of the web, many tasks performed on it are…
How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
Xinnan Dai, Haohao Qu, Yifen Shen +6
Benchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research. Recent studie…
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation
Haohao Qu, Yifeng Zhang, Liangbo Ning +2
Sequential recommendation methods are crucial in modern recommender systems for their remarkable capability to understand a user's changing interests based on past interactions. Ho…
A Survey of Mamba
Haohao Qu, Liangbo Ning, Rui An +5
As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions…
TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation
Haohao Qu, Wenqi Fan, Zihuai Zhao +1
There is a growing interest in utilizing large-scale language models (LLMs) to advance next-generation Recommender Systems (RecSys), driven by their outstanding language understand…