Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation
- Chen, Honglong, Li, Zhe, Wang, Zhu, Ni, Zhichen, Li, Junjian, Xu, Ge, Aziz, Abdul, Xia, Feng
- Authors: Chen, Honglong , Li, Zhe , Wang, Zhu , Ni, Zhichen , Li, Junjian , Xu, Ge , Aziz, Abdul , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: World Wide Web Vol. 25, no. 5 (2022), p. 1863-1882
- Full Text:
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- Description: The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Authors: Chen, Honglong , Li, Zhe , Wang, Zhu , Ni, Zhichen , Li, Junjian , Xu, Ge , Aziz, Abdul , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: World Wide Web Vol. 25, no. 5 (2022), p. 1863-1882
- Full Text:
- Reviewed:
- Description: The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Exploring the application of artificial neural network in rural streamflow prediction - A feasibility study
- Choudhury, Tanveer, Wei, Jackie, Barton, Andrew, Kandra, Harpreet, Aziz, Abdul
- Authors: Choudhury, Tanveer , Wei, Jackie , Barton, Andrew , Kandra, Harpreet , Aziz, Abdul
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 753-758
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- Description: Streams and rivers play a critical role in the hydrologic cycle with their management being essential to maintaining a balance across social, economic and environmental outcomes. Accurate streamflow predictions can provide benefits in many different ways such as water allocation decision making, flood forecasting and environmental watering regimes. This is particularly important in regional areas of Australia where rivers can play a critical role in irrigated agriculture, recreation and social wellbeing, major floods and sustainable environments. There are several hydrological parameters that effect stream flows in rivers and a major challenge with any prediction methodology, is to understand these parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required for accurate prediction of streamflow under usually unique, waterway-specific conditions using available data. This research employs an approach based on Artificial Neural Network (ANN) to provide this robust methodology. Data from readily available sources has been selected to provide appropriate input and output parameters to train, validate and optimise the neural network. The optimisation steps of the methodology are discussed and the predicted outputs are compared and analysed with respect to the actual collected values. © 2018 IEEE.
- Description: IEEE International Symposium on Industrial Electronics
- Authors: Choudhury, Tanveer , Wei, Jackie , Barton, Andrew , Kandra, Harpreet , Aziz, Abdul
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 753-758
- Full Text:
- Reviewed:
- Description: Streams and rivers play a critical role in the hydrologic cycle with their management being essential to maintaining a balance across social, economic and environmental outcomes. Accurate streamflow predictions can provide benefits in many different ways such as water allocation decision making, flood forecasting and environmental watering regimes. This is particularly important in regional areas of Australia where rivers can play a critical role in irrigated agriculture, recreation and social wellbeing, major floods and sustainable environments. There are several hydrological parameters that effect stream flows in rivers and a major challenge with any prediction methodology, is to understand these parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required for accurate prediction of streamflow under usually unique, waterway-specific conditions using available data. This research employs an approach based on Artificial Neural Network (ANN) to provide this robust methodology. Data from readily available sources has been selected to provide appropriate input and output parameters to train, validate and optimise the neural network. The optimisation steps of the methodology are discussed and the predicted outputs are compared and analysed with respect to the actual collected values. © 2018 IEEE.
- Description: IEEE International Symposium on Industrial Electronics
Graph learning : a survey
- Xia, Feng, Sun, Ke, Yu, Shuo, Aziz, Abdul, Wan, Liangtian, Pan, Shirui, Liu, Huan
- Authors: Xia, Feng , Sun, Ke , Yu, Shuo , Aziz, Abdul , Wan, Liangtian , Pan, Shirui , Liu, Huan
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Artificial Intelligence Vol. 2, no. 2 (2021), p. 109-127
- Full Text:
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- Description: Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in different areas such as social computing, information retrieval, computer vision, bioinformatics, economics, and e-commence. Second, it presents insights into open areas of research in the field. Third, it aims to stimulate new research ideas and more interests in graph learning. © IEEE Transactions on Artificial Intelligence 2020.
- Authors: Xia, Feng , Sun, Ke , Yu, Shuo , Aziz, Abdul , Wan, Liangtian , Pan, Shirui , Liu, Huan
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Artificial Intelligence Vol. 2, no. 2 (2021), p. 109-127
- Full Text:
- Reviewed:
- Description: Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in different areas such as social computing, information retrieval, computer vision, bioinformatics, economics, and e-commence. Second, it presents insights into open areas of research in the field. Third, it aims to stimulate new research ideas and more interests in graph learning. © IEEE Transactions on Artificial Intelligence 2020.
MAM : a metaphor-based approach for mental illness detection
- Zhang, Dongyu, Shi, Nan, Peng, Ciyuan, Aziz, Abdul, Zhao, Wenhong, Xia, Feng
- Authors: Zhang, Dongyu , Shi, Nan , Peng, Ciyuan , Aziz, Abdul , Zhao, Wenhong , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st International Conference on Computational Science, ICCS 2021 Vol. 12744 LNCS, p. 570-583
- Full Text:
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- Description: Among the most disabling disorders, mental illness is one that affects millions of people across the world. Although a great deal of research has been done to prevent mental disorders, detecting mental illness in potential patients remains a considerable challenge. This paper proposes a novel metaphor-based approach (MAM) to determine whether a social media user has a mental disorder or not by classifying social media texts. We observe that the social media texts posted by people with mental illness often contain many implicit emotions that metaphors can express. Therefore, we extract these texts’ metaphor features as the primary indicator for the text classification task. Our approach firstly proposes a CNN-RNN (Convolution Neural Network - Recurrent Neural Network) framework to enable the representations of long texts. The metaphor features are then applied to the attention mechanism for achieving the metaphorical emotions-based mental illness detection. Subsequently, compared with other works, our approach achieves creative results in the detection of mental illnesses. The recall scores of MAM on depression, anorexia, and suicide detection are the highest, with 0.50, 0.70, and 0.65, respectively. Furthermore, MAM has the best F1 scores on depression and anorexia detection tasks, with 0.51 and 0.71. © 2021, Springer Nature Switzerland AG.
- Authors: Zhang, Dongyu , Shi, Nan , Peng, Ciyuan , Aziz, Abdul , Zhao, Wenhong , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st International Conference on Computational Science, ICCS 2021 Vol. 12744 LNCS, p. 570-583
- Full Text:
- Reviewed:
- Description: Among the most disabling disorders, mental illness is one that affects millions of people across the world. Although a great deal of research has been done to prevent mental disorders, detecting mental illness in potential patients remains a considerable challenge. This paper proposes a novel metaphor-based approach (MAM) to determine whether a social media user has a mental disorder or not by classifying social media texts. We observe that the social media texts posted by people with mental illness often contain many implicit emotions that metaphors can express. Therefore, we extract these texts’ metaphor features as the primary indicator for the text classification task. Our approach firstly proposes a CNN-RNN (Convolution Neural Network - Recurrent Neural Network) framework to enable the representations of long texts. The metaphor features are then applied to the attention mechanism for achieving the metaphorical emotions-based mental illness detection. Subsequently, compared with other works, our approach achieves creative results in the detection of mental illnesses. The recall scores of MAM on depression, anorexia, and suicide detection are the highest, with 0.50, 0.70, and 0.65, respectively. Furthermore, MAM has the best F1 scores on depression and anorexia detection tasks, with 0.51 and 0.71. © 2021, Springer Nature Switzerland AG.
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