Agricultural Product Rating Prediction Design Based on Collaborative Filtering Algorithm

DOI

10.2991/cecs-18.2018.1How to use a DOI?

Keywords

agricultural e-commerce, collaborative filtering algorithms, rating prediction, RMSE and MAE.

Abstract

The rapid development of agricultural e-commerce has led to more and more agricultural products on the website, and the problem of information overload has become increasingly prominent. By analyzing the user's historical score data set, this paper analyzes the recommended models of different collaborative filtering algorithms and different similarity calculation formulas, predicts the rating of the new agricultural products by the given user, tests the RMSE and MAE values under different algorithms, and helps the users to recommend the preferred agricultural products, thus providing personalized service to the users.

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/).

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TY  - CONF
AU  - Bairui Tao
AU  - Ding Liu
AU  - Fengjuan Miao
AU  - Tongri Sun
PY  - 2018/07
DA  - 2018/07
TI  - Agricultural Product Rating Prediction Design Based on Collaborative Filtering Algorithm
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 1
EP  - 5
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.1
DO  - 10.2991/cecs-18.2018.1
ID  - Tao2018/07
ER  -