Research on Intelligent Knowledge Recommendation System for Police Applications

Ji Jiang; Jian Gang Tang

doi:10.4028/www.scientific.net/AMM.530-531.447

Paper Titles

PCA-ANFIS Based Prediction for Water Injection Effectiveness Cycle in Oil Fields
p.422

Power Information System Risk Assessment Method Based on Genetic Algorithms and Neural Network
p.429

Randomness Evaluation for Scrambled Image Using Intersecting Cortical Model Neural Network
p.434

Rapid Gradient Projection Algorithm for Image Reconstruction
p.443

Research on Intelligent Knowledge Recommendation System for Police Applications
p.447

Simulations of Linearity of Buffon’s Needle Problem
p.452

Steel Strip Defect Detection Based on Human Visual Attention Mechanism Model
p.456

Study on Data Fusion and Optimization Based on Neural Networks
p.463

Study on the Positioning Error of Turntable Based on Machine Vision System
p.467

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 530-531Research on Intelligent Knowledge Recommendation...

Research on Intelligent Knowledge Recommendation System for Police Applications

Abstract:

This paper proposed knowledge content tag recommendation algorithm in cloud computing Environment, and applied to police information knowledge. The algorithm analyzed user behavior history of operation and considered the similarity knowledge of the entries on the tag of police information, marked weight of tag in predicting when a user rating. On this basis, the police information implementations specific recommendations based on the specific user application knowledge. Meanwhile, combined the tag of system entry contents correlation with user correlation analysis, and solved the problems of system sparse matrix. Finally, the results demonstrated the effectiveness and superiority of recommendation algorithm.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 530-531)

Pages:

447-451

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.530-531.447

Citation:

Cite this paper

Online since:

February 2014

Authors:

Ji Jiang*, Jian Gang Tang

Keywords:

Content Similarity, Knowledge Recommended, Police Information Knowledge, Sparse Matrix

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Balabanovic. M, Shoham.Y. Fab: Conten-Based, collaborative recommendation. Communications of the ACM, 1997, 40(3): 66-72.

Google Scholar

[2] Basu C, Hirsh H, Cohen W. Recommendation as classification: Using social and content-based information in recommendation．In: Proc. of the AAAI＇98. Menlo Park: AAAI Press, 1998: 714-720.

Google Scholar

[3] Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, SartinM. Combining content－based and collaborative filters in an online newspaper. In: Proc. of the ACM SIGIR'99 WorkshopRecommender Systems: Algorithms and Evaluation. New York: ACM Press, (1999).

Google Scholar

[4] Pazzani M. A framework for collaborative, content－based, anddemographic filtering． Artificial Intelligence Review, 1999, 13.

Google Scholar

[5] Wei-ping Wang, Jin-hui Wang. Tag and hybrid recommendation method based on collaborative filtering [J]: Computer Engineering, 2011, 37(14): 34-38.

Google Scholar

[6] Yan-fei Xu, Liang Zhang, Wei Liu. Recommended based on collaborative personalized tag[J]: Computer Applications and Software, 2008, 25(1): 9-13.

Google Scholar

[7] Information on http: /movielens. umn. edu. during the seven-month period from September 19th, 1997 through April 22nd, (1998).

Google Scholar

[8] Pan Wei. Research on personalized courses recommendation system based on collaborative filtering technology[J]. Journal of Modern Information, 2009 (5): 193-196.

Google Scholar