Enhancing e-commerce recommender system adaptability with online deep controllable Learning-To-Rank
In the past decade, recommender systems for e-commerce have witnessed significant advancement. Recommendation scenarios can be divided into different type (e.g., pre-, during-, post-purchase, campaign, promotion, bundle) for different user groups or different businesses. For different scenarios, the...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://ojs.aaai.org/index.php/AAAI/article/view/17785 https://hdl.handle.net/10356/152717 |