61 citations · 201 across the 37 of their papers we have counts for
4 papers · 1 filter
BPL: Bias-adaptive Preference Distillation Learning for Recommender System
SeongKu Kang, Jianxun Lian, Dongha Lee +6
Recommender systems suffer from biases that cause the collected feedback to incompletely reveal user preference. While debiasing learning has been extensively studied, they mostly…
The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment
HyunJin Kim, DongHyun Ryu, Xiaoyuan Yi +6
The emergence of large language models (LLMs) has sparked discussion on Artificial Superintelligence (ASI), a hypothetical AI system that surpasses human intelligence. Although ASI…
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems
Xiao Zhou, Danyang Liu, Jianxun Lian +1
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback d…
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang +3
Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocit…