Traffic information distribution model based on crowdsourcing UGC and user trust degree

Author(s):  
Chenbo Deng ◽  
Chunxiao Fan ◽  
Zhigang Wen
2021 ◽  
pp. 1-17
Author(s):  
Fátima Leal ◽  
Bruno Veloso ◽  
Benedita Malheiro ◽  
Juan Carlos Burguillo ◽  
Adriana E. Chis ◽  
...  

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.


2021 ◽  
Author(s):  
Xiaobin Li ◽  
Haoran Liu

Abstract The emergence of 5G has promoted the rapid development of the Internet of Things(IOT), the dramatic increasing of mobile equipment has led to the increasing shortage of spectrum resources, D2D(Device-to-Device) communication technology is widely concerned for its ability to improve the utilization of spectrum resources. In order to expand the communication scope, relay nodes are introduced into D2D communications, as the third party of D2D relay communication, the quality of relay nodes directly affects the quality of communication process. In order to make more users willing to participate in relay communication, social relationship is introduced into D2D relay communication, However, as an explicit relationship between people, the function of social relationship in D2D communication is limited by the mobility of users and the variability of communication scenarios. In order to find a more reli­able relay node and upgrade the connection success rate of D2D relay communication, implicit social relationship between the users need to be mined. Aiming at that, user trust degree (UTD) is established in this paper. By combining the explicit relationship which is called the social connectivity degree with the implicit social relationship called the interest similarity degree, and considering the user’s movement, a relay selection algorithm is presented to help sender find a relay node with a deeper user trust, which can increase the user’s willingness to participate in D2D relay communication and upgrade the success rate of communication connection, so this algorithm can ensure the security of the relay node and can improve the throughput performance. Simulation results show that this algorithm can increase the success rate of connection, improve the overall throughput of the system and improve the user's communication expe­rience.


Author(s):  
Bruno P. Santos ◽  
Paulo H. L. Rettore ◽  
Heitor S. Ramos ◽  
Luiz F. M. Vieira ◽  
Antonio A. F. Loureiro

2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.


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