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Found 4 papers, 0 papers with code
Cluster Language Model for Improved E-Commerce Retrieval and Ranking: Leveraging Query Similarity and Fine-Tuning for Personalized Results
This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model.
De-Biased Modelling of Search Click Behavior with Reinforcement Learning
Our experiments demonstrate the effectiveness of the DRLC model in learning to reduce bias in click logs, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.
Online Product Feature Recommendations with Interpretable Machine Learning
For a customer, selecting the product that has the best trade-off between price and functionality is a time-consuming step in an online shopping experience, and customers can be overwhelmed by the available choices.
BIG-bench Machine Learning
Interpretable Machine Learning
+2
APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization
Query categorization is an essential part of query intent understanding in e-commerce search.
