Improving Collaborative Filtering Recommendation

DOI

10.2991/caai-18.2018.25How to use a DOI?

Keywords

data sparsity, scalability, Word2Vec, self- constructing clustering, word vectors

Abstract

Collaborative filtering recommender systems traditionally recommend products to users solely based on the user-item rating matrix and are simple, convenient to use. In this paper, we focus on two main issues, data sparsity and scalability. Data sparsity can lead to inaccurate recommendations, while scalability may cause an unacceptably long delay before valuable recommendations are acquired. We propose a novel approach to deal with these two issues. Word2Vec is employed to build item vectors from the user comments. Through the user-item rating matrix, user vectors of all the users are then obtained. A clustering technique is applied to reduce the time complexity related to the large numbers of items and users. Experimental results of real data sets are shown to demonstrate the effectiveness of our proposed approach.

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/).

Download article (PDF)

TY  - CONF
AU  - Pang-Ming Chu
AU  - Hong-Ru Tsai
AU  - Shie-Jue Lee
AU  - Shing-Tai Pan
PY  - 2018/08
DA  - 2018/08
TI  - Improving Collaborative Filtering Recommendation
BT  - Proceedings of the 2018 3rd International Conference on Control, Automation and Artificial Intelligence (CAAI 2018)
PB  - Atlantis Press
SP  - 106
EP  - 108
SN  - 2589-4919
UR  - https://doi.org/10.2991/caai-18.2018.25
DO  - 10.2991/caai-18.2018.25
ID  - Chu2018/08
ER  -