Name
Abstract | ||
|---|---|---|
Recommender systems play an important role in e-commerce websites as they improve the customer journey by helping the users find what they want at the right moment. In this paper, we focus on identifying a complementary relationship between the products of an e-commerce company. We propose a content-based recommender system for detecting complementary products, using Siamese Neural Networks (SNN). To this end, we implement and compare two different models: Siamese Convolutional Neural Network (CNN) and Siamese Long Short-Term Memory (LSTM). Moreover, we propose an extension of the SNN approach to handling millions of products in a matter of seconds, and we reduce the training time complexity by half. In the experiments, we show that Siamese LSTM can predict complementary products with an accuracy of ~85% using only the product titles. |
Year | Venue | DocType |
|---|---|---|
2021 | EACL | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marina Angelovska | 1 | 0 | 0.68 |
| Sina Sheikholeslami | 2 | 0 | 0.34 |
| Bas Dunn | 3 | 0 | 0.34 |
| Amir H. Payberah | 4 | 0 | 0.34 |