Case-Intelligence Recommendation on Massive Contents Processing through Dynamic Computing

DOI

10.2991/meic-14.2014.234How to use a DOI?

Keywords

Collusive attack detection; Reputation Aggregation; Relationship; Social Network; Collusion factor

Abstract

How to suggest a valid recommend within a reasonable time is the greatest technical challenge for the recommendation system, for which tremendous user cases with high dimension are generated while it runs in real time, and these massive data are too difficult to compute directly. This paper proposes a case -intelligence system framework along with a feature -based multi -layer feed -forward neural networks (MFNN) to succeed case- retrieval based on dynamic computing, which constructs the neural networks dependence on the real input vectors instead of the fixed and dull networks structure presupposed, and can apply many kinds of knowledge granularity from various levels effectively to help users for information retrieval and case adaptation. Our subsequent experimental results indicate that it is capable of handling the massive personalized data, and our covering algorithm can decrease the complexity of MFNN algorithm for dynamic computing, which performs adaptable knowledge granularity to enhance the system's efficiency of reasoning.

Copyright

© 2014, 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  - Rui Li
AU  - Jianyang Li
AU  - Benkun Zhu
PY  - 2014/11
DA  - 2014/11
TI  - Case-Intelligence Recommendation on Massive Contents Processing through Dynamic Computing
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 1050
EP  - 1054
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.234
DO  - 10.2991/meic-14.2014.234
ID  - Li2014/11
ER  -