| Issue |
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
|
|
|---|---|---|
| Article Number | 05006 | |
| Number of page(s) | 4 | |
| Section | Session 5: Algorithms and Simulation | |
| DOI | https://doi.org/10.1051/itmconf/20160705006 | |
| Published online | 21 November 2016 | |
The Random Walk Model Based on Bipartite Network
1 School of computer science and engineering, Hebei University of Technology
2 Hebei province key laboratory of big data calculation, China
a e-mail: zhangmandun@scse.hebut.edu.cn
b e-mail: 846176284@qq.com
c e-mail: zhaoJwvsMike@outlook.com
Corresponding Author: Jianhong Ma, majianhong@scse.hebut.edu.cn
With the continuing development of the electronic commerce and growth of network information, there is a growing possibility for citizens to be confused by the information. Though the traditional technology of information retrieval have the ability to relieve the overload of information in some extent, it can not offer a targeted personality service based on user’s interests and activities. In this context, the recommendation algorithm arose. In this paper, on the basis of conventional recommendation, we studied the scheme of random walk based on bipartite network and the application of it. We put forward a similarity measurement based on implicit feedback. In this method, a uneven character vector is imported(the weight of item in the system). We put forward a improved random walk pattern which make use of partial or incomplete neighbor information to create recommendation information. In the end, there is an experiment in the real data set, the recommendation accuracy and practicality are improved. We promise the reality of the result of the experiment
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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