Основные понятия
Complementary recommendation in e-commerce involves modeling relationships between products to enhance user experience and merchant sales.
Аннотация
The content discusses the definition, modeling approaches, research significance, challenges, and contributions of complementary recommendations in e-commerce. It covers various scenarios, such as electronic platforms, sports apps, travel platforms, and takeaway services. The analysis includes diverse methods like product content-based learning, user purchase sequence-based learning, and product relationship graph-based learning.
- Introduction to complementary recommendation in e-commerce.
- Modeling simple and complex complementary relationships.
- Addressing challenges like data sources, focus areas, and limitations.
- Discussing the importance of complementary recommendations for merchants, users, and platforms.
- Providing insights on future research directions and prospects.
Статистика
34の代表的な研究を比較して、製品間の補完関係をモデリングする方法について説明します。
機械学習とニューラルネットワーク技術が主に使用されます。