Enhancing E-commerce with Hierarchical Recommendation System
Enriching user shopping history improves recommendation accuracy in e-commerce platforms.
End-to-end Multi-Modal Product Matching in Fashion E-commerce: A Robust Approach
Utilizing a multi-modal approach with pretrained image and text encoders, a robust product matching system in the fashion e-commerce industry achieves state-of-the-art results.
Multimodal Approach for E-Commerce Product Description Generation
The author proposes a Multimodal In-Context Tuning approach, ModICT, to enhance the accuracy and diversity of product descriptions by leveraging visual and textual information.
Contrastive Pre-training for Deep Session Data Understanding: Leveraging Rich Information in E-commerce Sessions
The author proposes a Universal User Behavior Model (UBM) pre-trained on session data, utilizing contrastive learning to capture complex relations and dependencies, leading to significant performance gains across various downstream tasks.
Large Language Model based Long-tail Query Rewriting in Taobao Search: Bridging the Semantic Gap for Improved E-commerce Search
BEQUE is a comprehensive framework designed to bridge the semantic gap in long-tail queries, enhancing e-commerce search results.
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