45 citations · 72 across the 8 of their papers we have counts for
8 papers
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems
Pan Li, Yuyan Wang, Ed H. Chi +1
Recommending novel content, which expands user horizons by introducing them to new interests, has been shown to improve users' long-term experience on recommendation platforms \cit…
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
Pan Li, Yuyan Wang, Ed H. Chi +1
Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit…
URM4DMU: an user represention model for darknet markets users
Hongmeng Liu, Jiapeng Zhao, Yixuan Huo +5
Darknet markets provide a large platform for trading illicit goods and services due to their anonymity. Learning an invariant representation of each user based on their posts on di…
Latent User Intent Modeling for Sequential Recommenders
Bo Chang, Alexandros Karatzoglou, Yuyan Wang +3
Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with base…
Learning to Augment for Casual User Recommendation
Jianling Wang, Ya Le, Bo Chang +3
Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large bod…
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
Yuyan Wang, Xuezhi Wang, Alex Beutel +3
As multi-task models gain popularity in a wider range of machine learning applications, it is becoming increasingly important for practitioners to understand the fairness implicati…