35 citations · 97 across the 19 of their papers we have counts for
8 papers · 1 filter
Learning Robust Sequential Recommenders through Confident Soft Labels
Shiguang Wu, Xin Xin, Pengjie Ren +4
Sequential recommenders that are trained on implicit feedback are usually learned as a multi-class classification task through softmax-based loss functions on one-hot class labels.…
Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
Weiwei Sun, Zheng Chen, Xinyu Ma +6
Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons betwee…
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
Zihan Wang, Ziqi Zhao, Zhumin Chen +3
Few-shot named entity recognition (NER) has shown remarkable progress in identifying entities in low-resource domains. However, few-shot NER methods still struggle with out-of-doma…
Towards Explainable Conversational Recommender Systems
Shuyu Guo, Shuo Zhang, Weiwei Sun +3
Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficie…
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…
A Self-Correcting Sequential Recommender
Yujie Lin, Chenyang Wang, Zhumin Chen +6
Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendati…