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Recommender systems play a vital role in e-commerce. Existing algorithms usually learn ranking scores from a user's historical sequence, but they generally pay little attentions to mining the user's precise and varied interests to jointly ...

The early detection and processing of oversized ores on the conveyor belt can effectively prevent accidents such as material blockage, ensuring transportation efficiency and production safety. The long-used manual detection method, however, has ...

• Construct a dataset comprising 2543 images for oversized ore detection.
• Adopt rotated ellipse to replace horizontal box to fit the shape of adhesive ores.
• Use transfer learning, attention, and decoupled-head to improve model ...

User personalized recommendation is increasingly vital in many industrial applications. How to precisely mine user’s dynamic interests from multiple scenarios is a challenge task in Click-Through Rate (CTR) prediction. Existing works obtain ...

Problem classification serves as a fundamental process in a tax intelligence consulting system, enabling the categorization of user-posed questions according to their semantic attributes. This categorization is pivotal in ensuring accurate ...

Binary Neural Network (BNN) exhibits substantial potential for low-cost and energy-efficient deployment, notably in the application of BNN-facilitated object detection on embedded devices. Despite its high compression ratio, the optimization of ...

Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified sentence. Existing methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus ...

Object detection has been revolutionized by convolutional neural networks (CNNs), but their high computational complexity and heavy data access requirements make implementing these algorithms on edge devices challenging. To address this issue, we ...$$$$

The ratio of various types of ores on a conveyor belt indicates how well the ores are blended, which can play a crucial role in determining the quality of steel. However, the conventional method of artificial naked eye detection based on ...

Object detection is an important computer vision task with a wide range of applications, including autonomous driving, smart security, and other domains. However, the high computational requirements poses challenges on deploying object detection ...

Ore particle size information is a crucial indicator to evaluate the crushing quality and judge whether there are oversized ores on the conveyor belt. Accurately separating each ore is a critical prerequisite for obtaining high-precision particle ...

Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-world applications and have received considerable attention in recent years. During the process of solving DMOPs, tracking the constantly changing Pareto optimal set (POS) ...

Evolutionary constrained multiobjective optimization has received extensive attention and research in the past two decades, and a lot of benchmarks have been proposed to test the effect of the constrained multiobjective evolutionary algorithms (CMOEAs). ...

Feature explanation plays an increasingly essential role in the e-commerce search platform. Most of the existing studies focus on modeling the user’s interests to estimate the click-through rate (CTR). A good e-commerce system not only needs precise ...

Binary CNN-based object detector, largely saving storage and computation costs, has received high attention recently for the potention of efficient deployment on resource-constrained devices. However, previous designs often suffer from ...

Click through rate (CTR) and Conversion Rate (CVR) are core tasks in e-commerce recommender systems. Sequence behavior and multi-task learning have been widely used in CTR and CVR. Based on the concept of a transformer, we develop a technique of ...

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary algorithms, the historical moving directions of some special points along the Pareto front, such as the center and knee points, are widely employed to predict the Pareto-...

Recognition of Traditional Chinese Medicine (TCM) entities from different types of literature is challenging research, which is the foundation for extracting a large amount of TCM knowledge existing in unstructured texts into structured formats. The lack ...

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• An approach to normalize traditional Chinese medicine symptoms.
• Utilize ...

Traditional Chinese medicine (TCM) symptom normalization is difficult because the challenges of the symptoms having different literal descriptions, one-to-many symptom descriptions and different symptoms sharing a similar literal ...

Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based ...

In the rapid development of various technologies at the present stage, representative artificial intelligence technology has developed more prominently. Therefore, it has been widely applied in various social service areas. The application of artificial ...