Research on the Method and Application of Intelligent Information Service Demand Identification of Inland Waterway

The implementation of WIFI technology in underground mobile intelligent information terminal system(UMIITS) was studied in this paper. Because of using Mine Information Service Center as the core and handheld intelligent terminal as the platform, this system can realize the query and feedback of the mine production and safety information. Miner's position and navigation, the level of production informatization would be improved with it.

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Research on Collaborative Decision-Making Optimization of Intelligent Information Service Alliances

2008 Second International Symposium on Intelligent Information Technology Application ◽

10.1109/iita.2008.124 ◽

2008 ◽

Author(s):

Xiong Li

Keyword(s):

Decision Making ◽

Information Service ◽

Collaborative Decision Making ◽

Intelligent Information ◽

Collaborative Decision

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Personalized service recommendations for travel using trajectory pattern discovery

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718767845 ◽

2018 ◽

Vol 14 (3) ◽

pp. 155014771876784 ◽

Cited By ~ 5

Author(s):

Zongtao Duan ◽

Lei Tang ◽

Xuehui Gong ◽

Yishui Zhu

Keyword(s):

Information Service ◽

Traffic Information ◽

Test Results ◽

Success Rates ◽

Personalized Service ◽

Service Demand ◽

Global Positioning ◽

Push Forward ◽

Trajectory Measurement ◽

Spatiotemporal Trajectories

Service recommendations help travelers locate en route traffic information service of interest in a timely manner. However, recommendations based on simple traffic information, such as the number of requests for the location of a facility, fail to consider an individual’s preferences. Most existing work on improving service recommendations has continued to utilize the same ratings and rankings of services without consideration of diverse users’ demands. The challenge remains to push forward the modeling of spatiotemporal trajectories to improve service recommendations. In this research, we proposed a new method to address the above challenge. We developed a personalized service-trajectory correlation that could recommend the most appropriate services to users. In addition, we proposed the use of “congeniality” probability to measure the service demand similarity of two travelers based on their service-visiting behaviors and preferences. We employed a clustering-based scheme, taking into account the spatiotemporal dimensions to refine the trajectories at each spot where travelers stayed at a certain point in time. Experiments were conducted employing a real global positioning system–based dataset. The test results demonstrated that our proposed approach could reduce the deviation of the trajectory measurement to 10% and enhance the success rates of the service recommendations to 60%.

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Web4.0-Based Intelligent Information Service Modeling

2015 International Conference of Educational Innovation through Technology (EITT) ◽

10.1109/eitt.2015.23 ◽

2015 ◽

Author(s):

Xingben Yang ◽

Hongwen Xia

Keyword(s):

Information Service ◽

Service Modeling ◽

Intelligent Information

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Research on Intelligent Information System of Library under Big Data and Digitization Technology

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042063 ◽

2021 ◽

Vol 2083 (4) ◽

pp. 042063

Author(s):

Chunyu Wang ◽

Zhengyu Sha

Keyword(s):

Big Data ◽

Service System ◽

Academic Research ◽

Functional Model ◽

Information Service ◽

Accurate Identification ◽

Fine Grained ◽

Technology Services ◽

Four Levels ◽

Intelligent Information

Abstract In the big data environment, we develop personalized information of college libraries based on big data from three aspects: the overall architecture of the system model, the functional model of the system, and the design of system interface modules according to the design principles and requirements of the personalized information service system of the university library Service system design. In terms of the functional design of the platform, the service platform is divided into four levels: accurate identification of user needs based on big data, personalized customized services based on artificial intelligence, academic research and discussion space based on integrated media, and fine-grained subject resource aggregation based on knowledge. On this basis, a centralized model of individualized services of university libraries including internal and external personnel, information resources, technology, services, processes, platforms, and environment has been constructed.

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