A Survey of Recommender System from Data Sources Perspective

DOI

10.2991/meici-18.2018.2How to use a DOI?

Keywords

Recommender system; Rating; Text; Social network; Multiple data

Abstract

In order to solve the problem of information overload in big data era, the personalized recommender system has been widely used. Collaborative filtering, as a classical algorithm, has become the basis of the recommender system. In recent years, there are more and more recommender systems based on multiple data sources are proposed. Today’s recommender systems integrate multiple data sources and recommendation methods are more accurate and explainable compare with rating-based recommendation systems. How to integrate multiple data sources to further improve the accuracy and interpretability of recommendation results, reduce computational complexity and cold start risk has become the key content of recommendation researches.

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  - Huaiyu Pi
AU  - Zhenyan Ji
AU  - Chun Yang
PY  - 2018/12
DA  - 2018/12
TI  - A Survey of Recommender System from Data Sources Perspective
BT  - Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018)
PB  - Atlantis Press
SP  - 5
EP  - 9
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-18.2018.2
DO  - 10.2991/meici-18.2018.2
ID  - Pi2018/12
ER  -