Enhancing e-commerce recommender system adaptability with online deep controllable Learning-To-Rank

• Main Authors: Zeng, Anxiang, Yu, Han, He, Hualin, Ni, Yabo, Li, Yongliang, Zhou, Jingren, Miao, Chunyan
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; E-commerce; Learning To Rank
• Online Access: https://ojs.aaai.org/index.php/AAAI/article/view/17785; https://hdl.handle.net/10356/152717
• Institution: Nanyang Technological University

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...