Yanting ZhangDonghua University
Yanting Zhang
PhD
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35
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Publications (35)
In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were p...
Multi-behavior recommendation has gained significant attention in recent years for its ability to outperform single-behavior models. Current research related to multi-behavior models leaves room for improvement in the following two areas. First, the noise carried by individual behaviors and the additional noise generated during behavior processing...
Click-through rate (CTR) prediction is a critical task in recommender systems and online advertising systems. The extensive collection of behavior data has become popular for building prediction models by capturing user interests from behavior sequences. There are two types of entities involved in behavior sequences, users and items, which form thr...
Contextual bandits efficiently solve the exploration and exploitation (EE) problem in online recommendation tasks. Most existing contextual bandit algorithms utilize a fixed reward mechanism, which makes it difficult to accurately capture the preference changes of users in non-stationary environments, thus affecting recommendation performance. In t...
Recommender systems still face a trade-off between exploring new items to maximize user satisfaction and exploiting those already interacted with to match user interests. This problem is widely recognized as the exploration/exploitation (EE) dilemma, and the multi-armed bandit (MAB) algorithm has proven to be an effective solution. As the scale of...
Continuously detecting traffic signs in a video sequence is necessary for autonomous or assisted driving scenarios, since a vehicle needs the information from the signs to facilitate navigation. Single-image based traffic sign detector may fail in many cases, when the car moves fast on the road, resulting in motion blur, partial occlusion, and abru...
Click-through rate (CTR) is a positive feedback of user preferences or product purchases, and its small increase can bring huge benefits. Therefore, CTR prediction plays a key role in computing advertising and recommendation systems. Research shows that the accuracy of CTR prediction models is closely related to the input features. Existing related...
In recent years, spiking neural networks (SNNs), which originated from the theoretical basis of neuroscience, have attracted neuromorphic computing and brain-like computing due to their advantages, such as neural dynamics and coding mechanism, which are similar to biological neurons. SNNs have become one of the mainstream frameworks in the field of...
Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consi...
The technology for simultaneous localization and mapping (SLAM) has been well investigated with the rising interest in autonomous driving. Visual odometry (VO) is a variation of SLAM without global consistency for estimating the position and orientation of the moving object through analyzing the image sequences captured by associated cameras. Howev...
This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the...
Human mobility behavior is far from random, and its indicators follow non-Gaussian distributions. Predicting human mobility has the potential to enhance location-based services, intelligent transportation systems, urban computing, and so forth. In this paper, we focus on improving the prediction accuracy of non-Gaussian mobility data by constructin...