Mobile marketing recommendation method based on user location feedback

Chunyong Yin,Shilei Ding,Jin Wang

doi:10.1186/s13673-019-0177-6

Abstract

Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users’ location-based behaviors are divided into different time windows. For each window, the extractor achieves users’ timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.

Highlights

In recent years, the e-commerce industry has developed rapidly with the popularization of the Internet
The experimental results show that the user preferences we build are more accurate and convolutional neural networks have strong capabilities of feature extraction and model generalization
The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of the location information in the recommendation

Summary

Introduction

The e-commerce industry has developed rapidly with the popularization of the Internet. At this time, famous e-commerce platforms such as Alibaba and Amazon were born. It is convenient for users to buy various products without leaving the home. E-commerce platform can generate a large amount of user location feedback data which contains a wealth of user preference information [1]. The users’ location information and shopping location information are considered as the third factor. In this case, you can only use the limited check-in data. The users’ location feedback behavior and the timeliness of behavior are often overlooked

Objectives

Results

Conclusion