10 citations · 31 across the 38 of their papers we have counts for
5 papers · 1 filter
Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground
Zhenyu Xu, Hailin Xu, Zhouyang Lu +11
Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical s…
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
Fangye Wang, Hansu Gu, Dongsheng Li +3
Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the pr…
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
Fangye Wang, Hansu Gu, Dongsheng Li +4
Click-through rate (CTR) prediction is widely used in academia and industry. Most CTR tasks fall into a feature embedding \& feature interaction paradigm, where the accuracy of CTR…
Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning
Shitong Duan, Xiaoyuan Yi, Peng Zhang +3
Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical conten…
RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents
Yubo Shu, Haonan Zhang, Hansu Gu +4
The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in Human-Computer Interaction (HCI) by tailoring content based o…