Multi-Source Learning for Sales Prediction
| Autor: | Hong-Han Shuai, Hui-Ju Hung, Jui-Yi Tsai, Kuen-Han Tsai, Ching-Chih Chang, Hsuan-Yu Kuo, Yau-Shian Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry 05 social sciences Feature extraction 02 engineering and technology Machine learning computer.software_genre Missing data Popularity Exponential growth 0502 economics and business 0202 electrical engineering electronic engineering information engineering Tensor decomposition 050211 marketing 020201 artificial intelligence & image processing The Internet Product (category theory) Artificial intelligence business computer Multi-source |
| Zdroj: | TAAI |
| DOI: | 10.1109/taai.2017.38 |
| Popis: | With the convenience and popularity of Internet, the sales on e-commerce platforms have grown significantly. This exponential growth generates massive chunks of data, which provides the opportunity to utilize the historical data for predicting the customers' behaviors and can thus offer better services. One of the major tasks is to correctly estimate the coming sales of products since the e-retailers can reserve the products in a smart way. However, even with massive data, it is still challenging to estimate the sale amount of each product due to 1) the complicated language structures, 2) difficulties in integrating the features, and 3) missing values. To address these issues, we propose a framework for predicting the sales, which contains two phases: 1) feature extraction from product attributes and reviews, and 2) tensor decomposition for multi-source learning. The experimental results show that our framework outperforms baselines by 73%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|