Collaborative Filtering Algorithm Based on Social Network Information Flow Model

DOI

10.2991/icsnce-16.2016.119How to use a DOI?

Keywords

Communication technology; Collaborative filtering; Multidimensional half; Semi markov process

Abstract

In order to improve the accuracy of personalized recommendation technology, the state space of multidimensional semi markov process definition "empty state " has been extended to multidimensional markov process, combining with the social network analysis theory and social network information flow model. The model describes the process flow of information in members of the network society. Then, based on social network information flow model, collaborative filtering is put forward to SMRR. By comprehensive considering user's preferences and clubs, the influence of network of other members SMRR prediction accuracy is significantly higher than the original algorithm.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mingwei Sun
AU  - Qiang Liu
AU  - Enyang Gao
PY  - 2016/07
DA  - 2016/07
TI  - Collaborative Filtering Algorithm Based on Social Network Information Flow Model
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 614
EP  - 617
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.119
DO  - 10.2991/icsnce-16.2016.119
ID  - Sun2016/07
ER  -