Name
Abstract | ||
|---|---|---|
The traditional collaborative filtering recommendation systems have many deficiencies, which make them incompetent in the domain of clothing recommendation; we proposed a new ClothNet model based on CNN, RNN, collaborative filtering and the characteristics of the fashion industry. The accuracy and generalization performance of this model are improved compared with traditional systems. The visual information integrated into the ClothNet model enables the recommendation system to alleviate the cold start problem, and new clothes can be added to the recommendation list faster through the visual information. The addition of temporal information enables ClothNet sharply capturing the impact of seasonal and time changes on user preferences. However, because RNN and CNN have the disadvantage of requiring a large amount of data, combining RNN and CNN will make the model more difficult to converge, so we have adopted the LearningToRank training mode and obtained good results. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/FAIA200706 | FUZZY SYSTEMS AND DATA MINING VI |
Keywords | DocType | Volume |
Convolutional Neural Network, Recurrent neural network, Recommend System, LearningToRank, Collaborative filtering | Conference | 331 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Xing | 1 | 0 | 0.68 |
| Zhike Han | 2 | 0 | 1.01 |
| Yichen Shen | 3 | 0 | 0.34 |