75 citations · 144 across the 19 of their papers we have counts for
19 papers
Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos
Junyu Lu, Deyi Ji, Liqun Liu +9
Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification an…
Distinguishing Right from Wrong in Debates: Attribution Analysis of Chinese Harmful Memes
Weiming Wang, Junyu Lu, Han Wang +5
Research on harmful meme detection has garnered significant attention, resulting in the development of numerous datasets and methods. However, progress in detecting Chinese harmful…
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…
Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition
Haohao Zhu, Junyu Lu, Zeyuan Zeng +4
Humor recognition aims to identify whether a specific speaker's text is humorous. Current methods for humor recognition mainly suffer from two limitations: (1) they solely focus on…
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…
Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection
Haohao Zhu, Xiaokun Zhang, Junyu Lu +2
Textual personality detection aims to identify personality traits by analyzing user-generated content. To achieve this effectively, it is essential to thoroughly examine user-gener…
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning
Yijie Shu, Xiaokun Zhang, Youlin Wu +3
News recommendation emerges as a primary means for users to access content of interest from the vast amount of news. The title clickbait extensively exists in news domain and incre…
Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect
Junyu Lu, Bo Xu, Xiaokun Zhang +4
Current methods of toxic language detection (TLD) typically rely on specific tokens to conduct decisions, which makes them suffer from lexical bias, leading to inferior performance…
FineRec:Exploring Fine-grained Sequential Recommendation
Xiaokun Zhang, Bo Xu, Youlin Wu +3
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews fo…
Disentangling ID and Modality Effects for Session-based Recommendation
Xiaokun Zhang, Bo Xu, Zhaochun Ren +3
Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence pa…
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
Haohao Zhu, Xiaokun Zhang, Junyu Lu +7
Textual personality detection aims to identify personality characteristics by analyzing user-generated content on social media platforms. Extensive psychological literature highlig…
Side Information-Driven Session-based Recommendation: A Survey
Xiaokun Zhang, Bo Xu, Chenliang Li +3
The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate v…
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor…
Beyond Co-occurrence: Multi-modal Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma +3
Session-based recommendation is devoted to characterizing preferences of anonymous users based on short sessions. Existing methods mostly focus on mining limited item co-occurrence…
KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions
Yunzhi Qiu, Xiaokun Zhang, Weiwei Wang +3
Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a l…
Hate Speech Detection via Dual Contrastive Learning
Junyu Lu, Hongfei Lin, Xiaokun Zhang +5
The fast spread of hate speech on social media impacts the Internet environment and our society by increasing prejudice and hurting people. Detecting hate speech has aroused broad…
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
Junyu Lu, Bo Xu, Xiaokun Zhang +3
The widespread dissemination of toxic online posts is increasingly damaging to society. However, research on detecting toxic language in Chinese has lagged significantly. Existing…
Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
Xiaokun Zhang, Bo Xu, Liang Yang +4
Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based…