Recommendation with Item Clustering Based Collaborative Filtering

DOI

10.2991/iccset-14.2015.87How to use a DOI?

Keywords

recommendation; collaborative filtering; clustering

Abstract

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.

Copyright

© 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/).

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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  -