2024
DOI: 10.1109/tcss.2023.3268682
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Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

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Cited by 17 publications
(4 citation statements)
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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%
“…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%
“…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%
“…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%