| Title |
Comparison of Transfer Learning Models for Goods Recommendation in User’s Online and Offline Shopping |
| Authors |
정종진(Jongjin Jung) ; 강동구(Donggu Kang) ; 김지연(Jiyeon Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.7.1087 |
| Keywords |
Recommendation; Deep Learning; Transfer Learning; FCN; k-NN; Linear SVM |
| Abstract |
Recently, deep learning based services has been actively developed in various fields. Especially, in the online distribution environment some Applications with deep learning based on large amounts of data and user information began to be applied to real systems. In this paper, we develop an application system which recommends goods after learning and predicting using deep learning by presenting good image that user wants to purchase in online market sites. At this time, we apply three revised models based on the transfer learning model for automatic image recognition. After image recognition of goods, the system produces candidate goods and requires filter condition input for goods he wants to purchase. Finally, the system recommends goods by content-based recommendation method. In this paper, we experiment three revised models of image recognition which are used as core techniques in the system. These models are fully connected network, k-nearest neighbors search and linear SVM and they act as a classifier within the transfer learning model architecture. We experiment and compare performance evaluation for the revised models. |