Recommendation with Item Clustering Based Collaborative Filtering
Xin Wang, Zhi Yu, Can Wang
Available Online January 2015.
- 10.2991/iccset-14.2015.87How to use a DOI?
- recommendation; collaborative filtering; clustering
Recommender systems are playing a more and more important roles in people’s daily life and collaborative filtering (short for CF) is a widely used approach in recommender systems. In practice, many E-commerce companies such as Amazon use CF to make recommendations. However, as the number of users and items grow larger and larger, CF are suffering two kinds of problems: sparsity and scalability. So in this paper, we propose an item clustering based CF to solve these two problems. The experiments show that our method outperforms the traditional CF in term of both predicting accuracy and running time.
- © 2015, 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 - Xin Wang AU - Zhi Yu AU - Can Wang PY - 2015/01 DA - 2015/01 TI - Recommendation with Item Clustering Based Collaborative Filtering BT - Proceedings of the 2014 International Conference on Computer Science and Electronic Technology PB - Atlantis Press SP - 391 EP - 394 SN - 2352-538X UR - https://doi.org/10.2991/iccset-14.2015.87 DO - 10.2991/iccset-14.2015.87 ID - Wang2015/01 ER -