5 citations · 9 across the 6 of their papers we have counts for
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cs.IR2024
Data Augmentation for Sequential Recommendation: A Survey
Yizhou Dang, Enneng Yang, Yuting Liu +4
As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the wides…
cs.CL2024★ 1 cited
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
Pengxiang Lan, Enneng Yang, Yuting Liu +3
Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, w…
cs.IR2024★ 1 cited
Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted t…