Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Background selection schema on deep learning-based classification of dermatological disease
J Zhou, Z Wu, Z Jiang, K Huang, K Guo… - Computers in Biology and …, 2022 - Elsevier
Skin diseases are one of the most common ailments affecting humans. Artificial intelligence
based on deep learning can significantly improve the efficiency of identifying skin disorders …
based on deep learning can significantly improve the efficiency of identifying skin disorders …
Veracity-aware and event-driven personalized news recommendation for fake news mitigation
Despite the tremendous efforts by social media platforms and fact-check services for fake
news detection, fake news and misinformation still spread wildly on social media platforms …
news detection, fake news and misinformation still spread wildly on social media platforms …
News recommendation via multi-interest news sequence modelling
A session-based news recommender system recommends the next news to a user by
modeling the potential interests embedded in a sequence of news read/clicked by her/him in …
modeling the potential interests embedded in a sequence of news read/clicked by her/him in …
A counterfactual collaborative session-based recommender system
Most session-based recommender systems (SBRSs) focus on extracting information from
the observed items in the current session of a user to predict a next item, ignoring the causes …
the observed items in the current session of a user to predict a next item, ignoring the causes …
Dual contrastive transformer for hierarchical preference modeling in sequential recommendation
Sequential recommender systems (SRSs) aim to predict the subsequent items which may
interest users via comprehensively modeling users' complex preference embedded in the …
interest users via comprehensively modeling users' complex preference embedded in the …
Modeling temporal positive and negative excitation for sequential recommendation
Sequential recommendation aims to predict the next item which interests users via modeling
their interest in items over time. Most of the existing works on sequential recommendation …
their interest in items over time. Most of the existing works on sequential recommendation …
High-level preferences as positive examples in contrastive learning for multi-interest sequential recommendation
The sequential recommendation task based on the multi-interest framework aims to model
multiple interests of users from different aspects to predict their future interactions. However …
multiple interests of users from different aspects to predict their future interactions. However …
Intention-aware user modeling for personalized news recommendation
Although tremendous efforts have been made in the field of personalized news
recommendations, how to accurately model users' reading preferences to recommend …
recommendations, how to accurately model users' reading preferences to recommend …
A systematical evaluation for next-basket recommendation algorithms
Next basket recommender systems (NBRs) aim to recommend a user's next (shop**)
basket of items via modeling the user's preferences towards items based on the user's …
basket of items via modeling the user's preferences towards items based on the user's …
Aspect-driven user preference and news representation learning for news recommendation
Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs),
ensuring smooth interactions and improving the driving experience. Listening to news is a …
ensuring smooth interactions and improving the driving experience. Listening to news is a …