The Research of TF-IDF Recommendation Algorithm of Colleges and Universities' Patent System

DOI

10.2991/mcei-17.2017.35How to use a DOI?

Keywords

Recommendation algorithm; Word segmentation system; Text recommendation algorithm

Abstract

The users in search of patent achievements, its demand is also often vague and broad, in order to meet the user's search request, and at the same time in order to improve the conversion efficiency of patent information in colleges and universities. The research is based on NIPIR which is word segmentation system uses that to separate TF-IDF (term frequency–inverse document frequency) from patent's introduction. Then, use the same method to get TF (term frequency) of search log of user, and build data of user' preferences. According to the value of user's preferences and patent's TF-IDF, the system active send user information.The system that translates potential into actual demand increase conversion efficiency of scientific research of college. That realizes friendly docking between scientific research institutions and application institutions improve technological innovation and promotion ability.

Copyright

© 2017, 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  - He Liu
AU  - Ping Li
AU  - Chenxi Li
PY  - 2017/12
DA  - 2017/12
TI  - The Research of TF-IDF Recommendation Algorithm of Colleges and Universities' Patent System
BT  - Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SP  - 164
EP  - 169
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.35
DO  - 10.2991/mcei-17.2017.35
ID  - Liu2017/12
ER  -