A Collaborative Filtering Recommendation Algorithm Based On Conditional Entropy Trust Model

DOI

10.2991/icmemtc-16.2016.84How to use a DOI?

Keywords

non-linear dependence; conditional entropy; similarity calculation; collaborative filtering.

Abstract

The traditional collaborative recommendation algorithm doesn't take the non-linear dependence between users into consider, which is not accurate enough in prediction. The collaborative filtering algorithm based on entropy can measure the nonlinear characteristics of users, but it can't reasonably describe the relationship between users and is subject to sparsity. To address this problem, conditional entropy trust model is proposed, which uses the conditional entropy to describe the non-linear dependence between users, and Laplace estimation is introduced to alleviate sparsity. A collaborative filtering algorithm based on the conditional entropy trust model (CECF) is designed. The experiments show that this algorithm doesn't increase the time complexity and significantly improve the degree of accuracy.

Copyright

© 2016, 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  - Yitao Wu
AU  - Xingming Zhang
AU  - Xiaofeng Qi
AU  - Erning Xiao
AU  - Liang Jin
PY  - 2016/04
DA  - 2016/04
TI  - A Collaborative Filtering Recommendation Algorithm Based On Conditional Entropy Trust Model
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 435
EP  - 441
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.84
DO  - 10.2991/icmemtc-16.2016.84
ID  - Wu2016/04
ER  -