Personalized recommendation method based on user behavior analysis

DOI

10.2991/amcce-17.2017.142How to use a DOI?

Keywords

User Behavior; Personalized Recommendation; Two Classification; Artificial Rules

Abstract

The characteristics of user's behavior in the real scene are analyzed, and a personalized recommendation method based on user behavior analysis is put forward. In the electronic commerce user behavior can be divided into clicking, purchasing, collecting, plussing shopping cart, etc. The current mainstream algorithm collaborative filtering algorithm can not deal with other acts in addition to the purchase behavior . Take the method based on the artificial rule and the improved hierarchical fusion model based on bagging, converting the problem to a two classification problem for predicting whether or not to buy and recommending to users. Experimental results show that the proposed method makes full use of the user's behavior information, avoiding the limitations of the traditional methods, so that the recommended effect is significantly improved.

Copyright

© 2017, 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  - Yu Wang
AU  - Jin Shang
AU  - Xiaofang Wu
AU  - MaoFu Liu
PY  - 2017/03
DA  - 2017/03
TI  - Personalized recommendation method based on user behavior analysis
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 802
EP  - 809
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.142
DO  - 10.2991/amcce-17.2017.142
ID  - Wang2017/03
ER  -