A Survey of Recommender System from Data Sources Perspective
- https://doi.org/10.2991/meici-18.2018.2How to use a DOI?
- Recommender system; Rating; Text; Social network; Multiple data
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/meici-18.2018.2 ID - Pi2018/12 ER -