175 citations · 453 across the 18 of their papers we have counts for
18 papers
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang +4
Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negativ…
Temporal Interest Network for User Response Prediction
Haolin Zhou, Junwei Pan, Xinyi Zhou +4
User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are…
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Junguang Jiang, Baixu Chen, Junwei Pan +4
Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks. Occasionally, learning multiple tasks simu…
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
Enneng Yang, Junwei Pan, Ximei Wang +6
Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have bee…
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
Zuowu Zheng, Xiaofeng Gao, Junwei Pan +4
In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are propo…
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
Chenxiao Yang, Qitian Wu, Jipeng Jin +3
Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackles a semi-supervised learning problem where most interaction data are unobserved.…