scholarly journals A Study on Information Technology Freelancer Matching with Exploiting Blockchain in Gig Economy

2021 ◽  
Vol 18 ◽  
pp. 455-461
Author(s):  
Jinho Lim ◽  
Kwansik Na ◽  
Seungcheon Kim
Keyword(s):  
Recommendation System ◽  
Service Providers ◽  
High Reliability ◽  
Information Source ◽  
Reputation System ◽  
Personalized Recommendation ◽  
Blockchain Technology ◽  
Gig Economy ◽  
User Reputation ◽  
Recruitment System

In this paper, we propose a freelancer matching of a recommended recruitment system in a situation in which the freelance type employment market defined by peer-to-peer transactions, mutual evaluation of freelancers and clients, time flexibility of service providers, and the use of service providers' tools and assets are expanding. In order to increase the reliability and accuracy of recommendation through reputation, we propose a reputation ranking technique for reputation system, which is a kind of personalized recommendation system, based on the blockchain technology. We propose a reputation system model suitable for recruitment matching service. We have aims to study the method of implicitly extracting user reputation information based on two factors suitable for word of mouth among information source reliability factors. In other words, this paper defines a method for automatically extracting two reliability factors of freelancers from past reputation information, and proposes a method for effectively predicting freelancer applicant’ reputation information using only the information of high-reliability evaluators.

To Adapt to Changes in the User Long-Term and Recent Interest of Personalized Recommendation Model

2013 ◽  
Vol 791-793 ◽  
pp. 2143-2146 ◽  
Author(s):  
Hua Yue Chen ◽  
Jing Pu
Keyword(s):  
Recommendation System ◽  
Information Source ◽  
Information Organization ◽  
Personalized Recommendation ◽  
Web Information Retrieval ◽  
Exponential Expansion ◽  
Personalized Services ◽  
Internet Information ◽  
Information Recommendation

With the development and popularization of the information superhighway, people are surrounded by the sea of information. Exponential expansion of Internet information resources, is the vast amounts of information source, its information organization is heterogeneous, diverse, distribution and other features. therefore, can provide users with effective information recommendation, help users to find the valuable information you need the personalized recommendation system won wide attention in the field of Web information retrieval, and also in actual personalization service system has been widely applied in this paper, the personalized services recommendation system architecture to do some research, proposed a distinguishing the user long-term interests and immediate interests provide information to recommend a new model of personalized recommendation.

scholarly journals A Two-Stage Neural Network-Based Cold Start Item Recommender

2021 ◽  
Vol 11 (9) ◽  
pp. 4243
Author(s):  
Chieh-Yuan Tsai ◽  
Yi-Fan Chiu ◽  
Yu-Jen Chen
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Service Providers ◽  
Cold Start ◽  
Computational Time ◽  
Two Stage ◽  
The Neural Network ◽  
Textual Description ◽  
Recommendation Algorithms ◽  
Content Information

Nowadays, recommendation systems have been successfully adopted in variant online services such as e-commerce, news, and social media. The recommenders provide users a convenient and efficient way to find their exciting items and increase service providers’ revenue. However, it is found that many recommenders suffered from the cold start (CS) problem where only a small number of ratings are available for some new items. To conquer the difficulties, this research proposes a two-stage neural network-based CS item recommendation system. The proposed system includes two major components, which are the denoising autoencoder (DAE)-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, a textual description of an item is used as auxiliary content information to represent the item. Then, the DAE is applied to extract the content features from high-dimensional textual vectors. With the compact content features, a CS item’s rating can be efficiently derived based on the ratings of similar non-CS items. Second, the NNCF predictor is developed to predict the ratings in the sparse user–item matrix. In the predictor, both spare binary user and item vectors are projected to dense latent vectors in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user–item matrix learning. Finally, appropriate item suggestions can be accurately obtained. The extensive experiments show that the DAE can significantly reduce the computational time for item similarity evaluations while keeping the original features’ characteristics. Besides, the experiments show that the proposed NNCF predictor outperforms several popular recommendation algorithms. We also demonstrate that the proposed CS item recommender can achieve up to 8% MAE improvement compared to adding no CS item rating.

scholarly journals Analysis of the Optimal Application of Blockchain-Based Smart Lockers in the Logistics Industry Based on FFD-SAGA and Grey Decision-Making

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 329
Author(s):  
Shen-Tsu Wang ◽  
Meng-Hua Li ◽  
Chun-Chi Lien
Keyword(s):  
Web Application ◽  
Success Factors ◽  
Service Providers ◽  
Cost Effective ◽  
Application Programming Interface ◽  
Fractional Factorial ◽  
Office Workers ◽  
Logistics Industry ◽  
Blockchain Technology ◽  
Multiple Attribute

Blockchain technology has been applied to logistics tracking, but it is not cost-effective. The development of smart lockers has solved the problem of repeated distribution to improve logistics efficiency, thereby becoming a solution with convenience and privacy compared to the in-store purchase and pickup alternative. This study prioritized the key factors of smart lockers using a simulated annealing–genetic algorithm by fractional factorial design (FFD-SAGA) and grey relational analysis, and investigated the main users of smart lockers by grey multiple attribute decision analysis. The results show that the Web application programming interface (API) concatenation and money flow provider are the key success factors of smart lockers, and office workers are the main users of the lockers. Hence, how to better meet the needs of office workers will be an issue of concern for service providers.

scholarly journals An Incentive-based Mechanism for Volunteer Computing Using Blockchain

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Ismaeel Al Ridhawi ◽  
Moayad Aloqaily ◽  
Yaser Jararweh
Keyword(s):  
Resource Sharing ◽  
Secure Communication ◽  
Service Providers ◽  
Volunteer Computing ◽  
Resource Usage ◽  
Content Filtering ◽  
Computing Device ◽  
Blockchain Technology ◽  
Smart Agriculture ◽  
Industrial Solutions

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

Deep learning based Blockchain solution for preserving privacy in future vehicles

2020 ◽  
pp. 1-14
Author(s):  
Varsha R ◽  
Meghna Manoj Nair ◽  
Siddharth M. Nair ◽  
Amit Kumar Tyagi
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Smart Devices ◽  
The Road ◽  
Blockchain Technology ◽  
Technological Advances ◽  
Previous Decade ◽  
Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

scholarly journals Towards Secure Network Computing Services for Lightweight Clients Using Blockchain

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Xu ◽  
Guojun Wang ◽  
Jidian Yang ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Service Providers ◽  
Security Analysis ◽  
Service Provisioning ◽  
Network Computing ◽  
Security Risks ◽  
Blockchain Technology ◽  
Computing Services ◽  
Viable Solution ◽  
Secure Network ◽  
Iot Devices

The emerging network computing technologies have significantly extended the abilities of the resource-constrained IoT devices through the network-based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain-based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off-chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on-chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.

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