An attention‐based category‐aware GRU model for the next POI recommendation

Liu, Yuwen; Pei, Aixiang; Wang, Fan; Yang, Yehui; Zhang, Xuyun; Wang, Hao; Dai, Hong-Ning; Qi, Lianyong; Ma, Rui

doi:10.1002/int.22412

Cited by 117 publications

(63 citation statements)

References 31 publications

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“…Similar to TIVA in (1), TEVA is also an n-dimensional vector. Likewise, for club CB, all its transfer fee expense forms a transfer expense vector (denoted by TEVB) as presented in ( 8) which is similar to TIVB in (2). TEVA = (Ex (A, p1, feeA,1), …, Ex (A, pn, feeA,n)) ( 7)…”

Section: Solution: Tptelsh+bmentioning

confidence: 99%

“…With the gradual developments and improvements of social and economic aspects of people all over the world, people tend to enjoy life after hard work through various entertainment activities, such as sports, travelling, TVs and so on [1][2][3][4]. Specifically, as an effective way to loosen people's body and soul, a considerable amount of sport activities (e.g., football, volleyball, tennis ball, swimming, skating, running and so on) have begun to come into people's daily life and played a critical role in enriching people's life manners.…”

Section: Introductionmentioning

confidence: 99%

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Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

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Section: Solution: Tptelsh+bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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“…In this situation, more people are paying their attentions to the improvement of life health quality. With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public so as to ensure the high-quality physical conditions in daily life [4][5][6] . As a result, more and more people are engaging in various sport activities and have gained another identity or role, i.e., the so-called players.…”

Section: Introductionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

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“…The fifth-generation (5G) wireless systems provide support for many applications, such as smart home, smart city, smart traffic, smart health care, industry 4.0, smart agriculture, unmanned aerial vehicles [1,2,3,4,5,6,7] and so on. Many special applications require ultra reliable low-latency communications (URLLC) for 5G systems.…”

Section: Introductionmentioning

confidence: 99%

Shortest Link Scheduling in Wireless Networks Under The Rayleigh Fading Model

Huang

et al. 2021

Preprint

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Many shortest link scheduling algorithms adopt non-fading SINR interference model, which assumes that the received signal power will always remain determinate as long as the transmission power of the corresponding sender is fixed. In fact, because environment always influences the propagation of radio signals, the received signal power is by no means a certain value. Rayleigh fading is a statistical model for radio signals propagation. It assumes that the strength of a signal on a receiver is a random variable, varying with the Rayleigh distribution. This paper proposes a shortest link scheduling algorithm under the Rayleigh fading model (SLSRF). The SLSRF partitions the wireless network area into hexagons and colors the hexagons with 3 different colors such that two neighboring hexagons have different colors. The senders of the links scheduled simultaneously are arranged in hexagons with the same color. The correctness of the SLSRF is proved through theoretical analysis, and the efficiency is illustrated by elaborate simulations. Our simulation results demonstrate that the schedule delay of SLSRF is less than that of some results under the non-fading SINR interference model. Furthermore, we extend the SLSRF to a distributed version, which is suitable for large wireless networks.

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An attention‐based category‐aware GRU model for the next POI recommendation

Cited by 117 publications

References 31 publications

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Shortest Link Scheduling in Wireless Networks Under The Rayleigh Fading Model

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