A5027253238- Recommender Systems and Techniques
- Topic Modeling
- Machine Learning in Healthcare
- Advanced Bandit Algorithms Research
- Advanced Graph Neural Networks
- Text and Document Classification Technologies
- Expert finding and Q&A systems
- Explainable Artificial Intelligence (XAI)
- Distributed Control Multi-Agent Systems
- COVID-19 diagnosis using AI
- Mental Health via Writing
- Autonomous Vehicle Technology and Safety
- Generative Adversarial Networks and Image Synthesis
- Neural Networks Stability and Synchronization
- Robotic Path Planning Algorithms
- Sentiment Analysis and Opinion Mining
- Multimodal Machine Learning Applications
- Data Mining Algorithms and Applications
- Biomedical Text Mining and Ontologies
- Smart Grid Energy Management
- Supercapacitor Materials and Fabrication
- Remote-Sensing Image Classification
- Transportation Planning and Optimization
- Caching and Content Delivery
- ECG Monitoring and Analysis
Xi'an Jiaotong University
2019-2025
City University of Hong Kong
2023-2025
Nanjing University of Posts and Telecommunications
2023-2025
Jilin Medical University
2024
Jilin University
2024
OriginWater (China)
2024
Kuaishou (China)
2024
Yunnan University
2021
State Key Laboratory of Rare Earth Materials Chemistry and Application
2020
Peking University
2020
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performance, and epidemic spread. Today, these many more can be at global scale thanks digital footprints that we generate when browsing Web or using social media platforms. Unfortunately, scientists often struggle access data primarily because it is proprietary, even shared with privacy guarantees, either no representative...
Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend next item based on user's historical interactions. However, sequential often faces problem of data sparsity, widely exists recommender systems. Besides, most users only interact with a few items, but existing SRS models underperform these users. Such problem, named long-tail user is still be resolved. Data augmentation distinct way alleviate two problems, they need...
With the explosive growth of commercial applications recommender systems, multi-scenario recommendation (MSR) has attracted considerable attention, which utilizes data from multiple domains to improve their performance simultaneously. However, training a unified deep system (DRS) may not explicitly comprehend commonality and difference among domains, whereas an individual model for each domain neglects global information incurs high computation costs. Likewise, fine-tuning on is inefficient,...
Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most existing works focus on a single hospital with abundant medical data. However, many small hospitals only have few records, hinders applying medication to real world. Thus, we seek explore more practical setting, i.e. , multi-center recommendation. In this but total number records large. Though may benefit from affluent it also faced challenge that...
This paper focuses on a nonlinear multi-agent system (MAS) in the presence of unknown dynamics and eavesdroppers. An output consensus control strategy is proposed based an extended state observer, which achieves leader-following privacy-preserving backstepping framework. Different from existing distributed average approaches that require each follower to exchange expose information its neighbours, our avoids requirement for communication during interaction by decomposing follower's into two...
Delivering superior search services is crucial for enhancing customer experience and driving revenue growth. Conventionally, systems model user behaviors by combining preference query item relevance statically, often through a fixed logical 'and' relationship. This paper reexamines existing approaches unified lens using both causal graphs Venn diagrams, uncovering two prevalent yet significant issues: entangled effects, collapsed modeling space. To surmount these challenges, our research...
With the rapid evolution of transformer architectures, researchers are exploring their application in sequential recommender systems (SRSs) and presenting promising performance on SRS tasks compared with former models. However, most existing transformer-based frameworks retain vanilla attention mechanism, which calculates scores between all item-item pairs. this setting, redundant item interactions can harm model consume much computation time memory. In paper, we identify sparse phenomenon...
Multi-domain recommendation and multi-task have demonstrated their effectiveness in leveraging common information from different domains objectives for comprehensive user modeling. Nonetheless, the practical usually faces multiple tasks simultaneously, which cannot be well-addressed by current methods. To this end, we introduce M3oE, an adaptive Multi-task Mixture-of-Experts framework. M3oE integrates multi-domain information, maps knowledge across tasks, optimizes objectives. We leverage...
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions many aspects our lives, especially for various human-oriented online services such as e-commerce platforms social media sites. In past few decades, rapid developments have significantly benefited human creating economic value, saving time effort, promoting good. However, recent studies found that...
The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence multimedia services, such as short video, news etc., understanding these contents while recommending becomes critical. Besides, multimodal features also helpful in alleviating problem data sparsity RS. Thus, Multimodal Recommender System (MRS) attracted much attention...
Graphene oxide-supported uniform cobalt tungstate nanoparticles (CoWO<sub>4</sub>/GO) were prepared, which can be used as catalyst precursors for the diameter-controlled growth of single-walled carbon nanotubes (SWCNTs).
In the era of information explosion, spatio-temporal data mining serves as a critical part urban management. Considering various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple attributes simultaneously can alleviate regulatory pressure foster smart city construction. However, current research not handle multi-attribute prediction well due to complex relationships between diverse attributes. The key challenge lies in how address common...
Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus LLM as RS, which may face challenge intolerant inference costs Recently, integration into known LLM-Enhanced Recommender Systems (LLMERS), garnered significant interest due to its address latency and memory constraints real-world applications. This paper presents...
Recommender systems aim to provide personalized suggestions users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview achieving trustworthy systems. It cover six important aspects: Safety & Robustness, Non-discrimination Fairness, Explainability, Privacy, Environmental Well-being,...
Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most existing works focus on a single hospital with abundant medical data. However, many small hospitals only have few records, hinders applying medication to real world. Thus, we seek explore more practical setting, i.e., multi-center recommendation. In this but total number records large. Though may benefit from affluent it also faced challenge that...
The recommendation of medication is a vital aspect intelligent healthcare systems, as it involves prescribing the most suitable drugs based on patient's specific health needs. Unfortunately, many sophisticated models currently in use tend to overlook nuanced semantics medical data, while only relying heavily identities. Furthermore, these face significant challenges handling cases involving patients who are visiting hospital for first time, they lack prior prescription histories draw upon....
Model editing aims to precisely modify the behaviours of large language models (LLMs) on specific knowledge while keeping irrelevant unchanged. It has been proven effective in resolving hallucination and out-of-date issues LLMs. As a result, it can boost application LLMs many critical domains (e.g., medical domain), where is not tolerable. In this paper, we propose two model studies validate them domain: (1) directly factual (2) explanations facts. Meanwhile, observed that current methods...