Research on Personalized Recommendation System for Graph Database

DOI

10.2991/ncce-18.2018.170How to use a DOI?

Keywords

Collaborative filtering, graph database, hot evaluation function.

Abstract

In the rapid development of the Internet, the abuse of Over Loading has become increasingly prominent in the production and life. Facing these challenges, recommender systems emerge as the times require. This paper first introduces the classic collaborative filtering recommendation algorithm and introduces a popular evaluation function to design a personalized recommendation algorithm based on graph database Neo4j and compares it with traditional relational database.

Copyright

© 2018, 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  - Yanjie Liang
PY  - 2018/05
DA  - 2018/05
TI  - Research on Personalized Recommendation System for Graph Database
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 1019
EP  - 1023
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.170
DO  - 10.2991/ncce-18.2018.170
ID  - Liang2018/05
ER  -