Titlebook: Web Information Systems and Applications; 21st International C Cheqing Jin,Shiyu Yang,Yong Zhang Conference proceedings 2024 The Editor(s)

巨大沒(méi)有

Genome

A Study on Context-Matching-Based Joint Training for Chinese Coreference Resolutiond Chinese coreference resolution models, a context-matching-based joint training Chinese coreference resolution model is proposed. The model utilizes RoBERTa(wwm)-large combined with BiLSTM to encode Chinese text, then clusters word embeddings. It uses the results of the word clustering to recognize

Lineage

神圣不可

DFCDR: Domain-Aware Feature Decoupling and?Fusion for?Cross-Domain Recommendationintroducing domain-specific preferences from the source domain can introduce irrelevant information to the target domain. Furthermore, directly combining domain-general and domain-specific information may hinder the performance of the target domain. In this paper, we propose a domain-aware feature d

Adj異類的

Two-Stage Enhancement for?Recommendation Systems Based on?Contrastive Learninghods often employ graph neural networks to process the relational networks and use contrastive learning to obtain more effective node representations. However, persistent challenges from active users’ noisy data and the cold-start problem related to inactive users impact model performance. Recent st

假設(shè)

無(wú)辜

abysmal

Popularity-Aware Graph Neural Network with?Global Context for?Session-Based Recommendationsystems model user preferences from the current session using graph neural networks but overlook the varying importance of items with different popularity. To address this, we propose the Popularity-aware Graph Neural Network with Global Context (PGNN-GC), which models popularity features to better

Terminal

會(huì)犯錯(cuò)誤