Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

Gao, Honghao; Wu, Yinchen; Xu, Yueshen; Li, Rui; Jiang, Zhiping

doi:10.1109/tcss.2023.3268682

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…This paper also uses the VRP considering demands and vehicle capacity and derives solutions by a mathematical model and a meta-heuristic algorithm. The development of the Internet has led to a vast amount of information about users, making it crucial to derive the direction of user preferences [27]. In this regard, this study can be seen as reflecting customer preference clearly and more empirical research because it sets the problems using several months of actual data from the 'L' medical company in South Korea.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

Jo,

Ko,

et al. 2024

Sustainability

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As society continues to age, people are becoming more concerned about their health care. This has led to an increase in the demand for medical products in urban areas, emphasizing the need for regular and prompt deliveries. However, the existing logistics centers are located in the suburbs of Seoul, a metropolitan city, which makes it challenging to ensure timely delivery. To address this issue, this study aims to establish new logistics centers in urban areas, particularly in Seoul, while minimizing CO2 emissions from delivery vehicles in alignment with sustainability efforts. The scientific gap addressed in and the novelty of this paper is that the input parameters are prepared based on actual data from a medical company in Korea to reflect reality, and the mathematical model-based optimization technique is applied to determine the optimal location of a new logistics center. The genetic algorithm is developed to solve the proposed mathematical model by deriving optimal or near-optimal solutions. Furthermore, the numerical experiment examined the impact of establishing a new logistics center in one of the candidate areas of local governments in Seoul by considering environmental impact. As a result, the new logistics network can reduce CO2 emissions by approximately 66.74% compared to the existing logistics network.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

Jo,

Ko,

et al. 2024

Sustainability

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“…Based on the cached data, more details can be drawn on the user preferences. Gao et al propose an NCF (Neural Collaborative Filtering) model to discover user preferences from biased user behavior sequences [ 89 ]. They map the data into an embedding space using a self-attention mechanism.…”

Section: Investigation Of Existing Workmentioning

confidence: 99%

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

et al. 2023

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With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.

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“…In addition, deep learning-based anomaly detection methods can also utilize models such as convolutional neural networks (CNN) [13] or recurrent neural networks (RNN) [14] to extract features and classify time series data, identifying anomalous events. Except methods mentioned above, there are also other methods that can be applied to time series data [15]. For example, support vector machines (SVM) [16] can be trained on historical data to predict and classify future data; wavelet transform can decompose time series data into different frequency components to identify anomalous events; chaotic theorybased methods can utilize concepts and methods from chaotic theory to extract features and classify time series data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

Song,

Li,

Chen

et al. 2024

CMES

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With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques. Nevertheless, the absence of accurate abnormal information in unsupervised learning methods limits their performance. To address these problems, we propose AS-GCN-MTM, an adaptive structural Graph Convolutional Networks (GCN)-based framework using a mean-teacher mechanism (AS-GCN-MTM) for anomaly identification. It performs better than unsupervised methods using only a small amount of labeled data. Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model. However, the dependencies between data are often unknown in time series data. To solve this problem, we designed a graph structure adaptive learning layer based on neural networks, which can automatically learn the graph structure from time series data. It not only better captures the relationships between nodes but also enhances the model's performance by augmenting key data. Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%, 36.1%, and 5.6%, respectively, on three real datasets with a 10% data labeling rate.

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Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

Cited by 17 publications

References 37 publications

Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

Efficient Expansion Algorithm of Urban Logistics Network for Medical Products Considering Environmental Impact

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

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