240 citations · 574 across the 54 of their papers we have counts for
54 papers
MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation
Xiangjin Xie, Yuxin Chen, Ruipeng Wang +8
Graph neural networks have been widely used in recent recommender systems, where negative sampling plays an important role. Existing negative sampling methods restrict the relation…
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Yangning Li, Shirong Ma, Xiaobin Wang +6
Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. Ho…
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
Tianyu Yu, Chengyue Jiang, Chao Lou +12
Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which…
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
Jinpeng Wang, Ziyun Zeng, Yunxiao Wang +7
The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommender…
LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles
Shulin Huang, Shirong Ma, Yinghui Li +4
With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities. But can they think out of the box? Do th…
MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities
Yangning Li, Tingwei Lu, Yinghui Li +5
The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class. Conventional ESE methods are based on mono-mod…
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Yinghui Li, Haojing Huang, Shirong Ma +5
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community. Benefiting from their emergent abilities, LLMs have…
Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation
Kai Ouyang, Xianghong Xu, Miaoxin Chen +4
Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR…
Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction
Yinghui Li, Shirong Ma, Shaoshen Chen +5
Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human daily life and various downstream tasks. Recent approaches mainly emp…
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Yinghui Li, Yong Jiang, Yangning Li +4
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given inp…
Exploring Lottery Prompts for Pre-trained Language Models
Yulin Chen, Ning Ding, Xiaobin Wang +4
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning. Give…
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Jingheng Ye, Yinghui Li, Qingyu Zhou +4
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possi…
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Tong Wu, Zhihao Fan, Xiao Liu +9
Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generatio…
Breaking the Curse of Knowledge: Towards Effective Multimodal Recommendation using Knowledge Soft Integration
Kai Ouyang, Chen Tang, Zenghao Chai +4
A critical challenge in contemporary recommendation systems lies in effectively leveraging multimodal content to enhance recommendation personalization. Although various solutions…
Investigating Graph Structure Information for Entity Alignment with Dangling Cases
Jin Xu, Yangning Li, Xiangjin Xie +4
Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which play an important role in knowledge engineering. Recently, EA with danglin…
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
Jifan Yu, Mengying Lu, Qingyang Zhong +9
Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although t…
From Retrieval to Generation: Efficient and Effective Entity Set Expansion
Shulin Huang, Shirong Ma, Yangning Li +2
Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target semantic class described by seed entities. Most existing ESE methods are retrieval-based fr…
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
Kai Ouyang, Wenhao Zheng, Chen Tang +2
Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suff…
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Yangning Li, Jiaoyan Chen, Yinghui Li +3
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attri…
Knowledge-augmented Few-shot Visual Relation Detection
Tianyu Yu, Yangning Li, Jiaoyan Chen +8
Visual Relation Detection (VRD) aims to detect relationships between objects for image understanding. Most existing VRD methods rely on thousands of training samples of each relati…
Establishing a stronger baseline for lightweight contrastive models
Wenye Lin, Yifeng Ding, Zhixiong Cao +1
Recent research has reported a performance degradation in self-supervised contrastive learning for specially designed efficient networks, such as MobileNet and EfficientNet. A comm…
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge
Yangning Li, Jiaoyan Chen, Yinghui Li +3
Contextual synonym knowledge is crucial for those similarity-oriented tasks whose core challenge lies in capturing semantic similarity between entities in their contexts, such as e…
Few-shot Classification with Hypersphere Modeling of Prototypes
Ning Ding, Yulin Chen, Ganqu Cui +4
Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct c…
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
Yangning Li, Yinghui Li, Xi Chen +3
Open Relation Extraction (OpenRE) aims to discover novel relations from open domains. Previous OpenRE methods mainly suffer from two problems: (1) Insufficient capacity to discrimi…