Trust-Enhanced Collaborative Filtering for Personalized Point of Interests Recommendation

Wang, Wei; Chen, Junyang; Wang, Jinzhong; Chen, Junxin; Liu, Jinquan; Gong, Zhiguo

doi:10.1109/tii.2019.2958696

Cited by 106 publications

(63 citation statements)

References 20 publications

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“…It is generally divided into weak AI, strong AI, and super AI. The key technologies of AI include Machine Learning (ML) [44], [45], Natural Language Processing (NLP), robot, computer vision and expert system. Among them, ML is the most core technology in AI.…”

Section: ) Artificial Intelligence (Ai)mentioning

confidence: 99%

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

et al. 2020

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With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT. INDEX TERMS Internet of medical things (IoMT), deep learning, edge of computing, computation offloading.

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Section: ) Artificial Intelligence (Ai)mentioning

confidence: 99%

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

et al. 2020

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“…Hu and Zheng 20 designed a multistage attention network to capture the different influence for multivariate time series prediction, where they mainly exploited the influential attention mechanism, temporal attention mechanism of model, and the prediction performance on two different real-world multivariate time series data sets. Wang et al 21 developed a trust-enhanced collaborative filtering for personalized point-of-interest (POI) recommendation, where the trust-enhanced user similarity in user-based collaborative filtering based on network representation learning is calculated, and these two factors into POI recommendation are integrated by a fusion model. However, the above existing detection methods can only detect a single complex event from event streams once a time, and they cannot realize the sharing detection for multiple complex events at the same time due to its unsharing characteristic.…”

Section: Related Workmentioning

confidence: 99%

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Wang

Lin³

et al. 2020

International Journal of Distributed Sensor Networks

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Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

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“…Empowered by Internet of Things (IoTs) technologies and advanced algorithms that can collect and handle massive traffic datasets, urban computing and intelligence can make more informed decisions and create feedback loops between actual traffic situation and management department in the urban environment [1]. It can bridge the gaps between ubiquitous sensing, intelligent computing, cooperative communication, and big data management technologies to create novel solutions which can improve urban traffic environments, quality of life, and smart city systems [2]. In these urban computing methods, the huge datasets used by the scientists are all from various sources, such as geographic information, taxi GPS, and online weather web sites [3].…”

Section: Introductionmentioning

confidence: 99%

Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

Lin

Gao

et al. 2020

Wireless Communications and Mobile Computing

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Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

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Trust-Enhanced Collaborative Filtering for Personalized Point of Interests Recommendation

Cited by 106 publications

References 20 publications

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

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