Jianyong Wang

For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely used for enhancing personalized recommendation. However, these interacted items have not only transferable features that match users’ interests and are beneficial to the recommendation domain, but also have features related to users’ unique intents in the search domain. Such a domain gap of item features is neglected by most current search-enhanced recommendation methods. They directly incorporate these search behaviors into recommendation, and thus introduce partial negative transfer. Tackling this problem is challenging due to the lack of explicit supervision signals to disentangle features matching search-specific intent or general interest. To address this, we propose ClardRec, a c ounterfactual l e a rning-driven r epresentation d isentanglement framework for search-enhanced recommendation, based on the common belief that a user would click an item under a query not solely because of the item-query match but also due to the item’s query-independent general features (e.g., color or style) that interest the user. These general features exclude the reflection of search-specific intents contained in queries, ensuring a pure match to users’ underlying interests to complement recommendation. We perform the disentanglement based on a counterfactual thinking idea, how would user preferences and query match change for items if we removed their query-related features in search. Specifically, we leverage search queries to construct counterfactual signals to disentangle item representations, isolating only query-independent general features. These representations subsequently enable feature augmentation and data augmentation for the recommendation scenario. Comprehensive experiments on real datasets demonstrate that ClardRec is effective in both collaborative filtering and sequential recommendation scenarios. The source code is available at https://github.com/JJCui96/ClardRec .

Aspect-based sentiment analysis (ABSA) constitutes a critical subtask within affective computing, whose central challenge involves the accurate and efficient identification of sentiment polarity associated with specific aspect terms in review sentences. Although syntactic knowledge has demonstrated significant benefits in traditional ABSA models, existing approaches based on large language models (LLMs) have largely overlooked such structural information and often fail to comprehensively model both implicit and explicit sentiment expressions. To bridge this gap, we propose TrustSyn, a novel framework designed to enhance LLMs with trustworthy, constituency-structured dependency knowledge for ABSA. Specifically, the input sentences are first parsed using both dependency and constituency parsers. The resulting syntactic information is then restructured into a unified and reliable representation through a trustworthy syntax integration process. This structured knowledge is formalized and injected into LLMs to augment their comprehension of aspect sentiment associations. To the best of our knowledge, this is the first work to integrate constituency-informed dependency structures into LLMs for ABSA. Finally, experimental results demonstrate that TrustSyn consistently outperforms state-of-the art models across five benchmark datasets. Further ablation studies and analyses confirm its robustness and strong generalization capability.

Automated electrocardiogram (ECG) classification tasks play a crucial role in clinical but face challenges due to the scarcity of accessible and well-labeled data. ECG data augmentation is an effective way to address these challenges, either by altering the characteristics of real ECG data or using statistical and generative models to generate labeled data. However, the generated data often suffer from noise in both the data and label, which can reduce the performance of classification models. To address this, we propose a novel self-iterative two-stage data augmentation method for automated ECG classification, called SiTs-ECG. In the generation stage, an unconditional diffusion model, guided by a Transformer encoder, is trained to capture the complex characteristics of long-term ECG signals, generating high-quality ECG-like samples. In the selection stage, the generated samples are assigned pseudo-labels by a well-trained base classification model, and those generated samples for which the model can confidently predict the pseudo-labels are selected. We then integrate these stages into a self-iterative training process to continually improve the performance of base classification model. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method. Notably, on the Apnea-ECG dataset, using ECG-Transformer as the downstream classification model, Precision, Recall, F1, and Accuracy are improved by 7.9, 9.1, 9.2, and 7.3 percentage points, respectively. Furthermore, our method is versatile and compatible with various generative and downstream classification models, showing promising applications in automated ECG classification in the clinical field.

Background: Electroencephalogram (EEG) is a promising, non-invasive method for identifying the presence of Alzheimer's disease (AD) by recognizing specific brain activity patterns associated with the disease. However, research on the correlation between EEG and the degree of cognitive impairment is still lacking. Objective: In this study, we employ machine learning models to explore the potential of EEG in distinguishing different levels of cognitive impairment and various types of dementia. Methods: A total of 431 participants, including 77 cognitively unimpaired (CU), 167 patients with mild dementia, 110 patients with moderate dementia, and 77 patients with severe dementia were enrolled. Among them, 91 patients have detailed biomarker results to support differential diagnosis, with 77 AD and 14 frontotemporal dementia. After feature extraction, the rule-based representation learning was used to train models for EEG-based classification tasks. Results: Our model can effectively differentiate between CU and moderate-to-severe dementia (AUC 0.8475), as well as between CU and AD patients in individuals under 65 (AUC 0.8170). However, our preliminary analysis was not able to effectively distinguish between different types of dementia. It is also challenging to differentiate between CU and mild dementia groups, as well as between the moderate and severe dementia. Conclusions: Our study suggests that EEG might be used not only in the early identification of AD, but also in the diagnosis and monitoring of the entire dementia spectrum, encompassing various stages and types of cognitive decline.