130 citations · 486 across the 38 of their papers we have counts for
8 papers · 1 filter
Better Pre-Training by Reducing Representation Confusion
Haojie Zhang, Mingfei Liang, Ruobing Xie +3
In this work, we revisit the Transformer-based pre-trained language models and identify two different types of information confusion in position encoding and model representations,…
Reweighting Clicks with Dwell Time in Recommendation
Ruobing Xie, Lin Ma, Shaoliang Zhang +2
The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title…
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
Xiaoyang Liu, Chong Liu, Pinzheng Wang +5
Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential compon…
Multi-granularity Item-based Contrastive Recommendation
Ruobing Xie, Zhijie Qiu, Bo Zhang +1
Contrastive learning (CL) has shown its power in recommendation. However, most CL-based recommendation models build their CL tasks merely focusing on the user's aspects, ignoring t…
Prompt Tuning for Discriminative Pre-trained Language Models
Yuan Yao, Bowen Dong, Ao Zhang +6
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks. However, to the best of ou…
Personalized Prompt for Sequential Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +4
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to…
Selective Fairness in Recommendation via Prompts
Yiqing Wu, Ruobing Xie, Yongchun Zhu +5
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and user…
Multi-view Multi-behavior Contrastive Learning in Recommendation
Yiqing Wu, Ruobing Xie, Yongchun Zhu +6
Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-…