1 citations · 1 across the 4 of their papers we have counts for
6 papers
IP2: Entity-Guided Interest Probing for Personalized News Recommendation
Youlin Wu, Yuanyuan Sun, Xiaokun Zhang +4
News recommender systems aim to provide personalized news reading experiences for users based on their reading history. Behavioral science studies suggest that screen-based news re…
Rethinking Contrastive Learning in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailin…
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
Xiaokun Zhang, Bo Xu, Chenliang Li +4
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts in…
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement
Junyu Lu, Kai Ma, Kaichun Wang +5
Large Language Models (LLMs) have become essential for offensive language detection, yet their ability to handle annotation disagreement remains underexplored. Disagreement samples…
Towards Comprehensive Detection of Chinese Harmful Memes
Junyu Lu, Bo Xu, Xiaokun Zhang +5
This paper has been accepted in the NeurIPS 2024 D & B Track. Harmful memes have proliferated on the Chinese Internet, while research on detecting Chinese harmful memes significant…
PclGPT: A Large Language Model for Patronizing and Condescending Language Detection
Hongbo Wang, Mingda Li, Junyu Lu +5
Disclaimer: Samples in this paper may be harmful and cause discomfort! Patronizing and condescending language (PCL) is a form of speech directed at vulnerable groups. As an essenti…