6 citations · 25 across the 33 of their papers we have counts for
7 papers · 1 filter
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…
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sijia Liu, Jiahao Liu, Hansu Gu +4
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of ite…
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…
Recommendation Unlearning via Matrix Correction
Jiahao Liu, Dongsheng Li, Hansu Gu +5
Recommender systems are important for providing personalized services to users, but the vast amount of collected user data has raised concerns about privacy (e.g., sensitive data),…
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Jiahao Liu, Dongsheng Li, Hansu Gu +4
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, seque…