Some Practice for Improving the Search Results of E-commerce
Fanyou Wu, Yang Liu, Rado Gazo, Benes Bedrich, Xiaobo Qu
TL;DR
This paper presents practical NLP-based methods to enhance e-commerce search results, demonstrating competitive performance in the Amazon KDD Cup 2022 across multiple tasks.
Contribution
It introduces a practical solution applying NLP techniques to improve search quality in e-commerce, achieving top rankings in a major competition.
Findings
Ranked 6th in task one
Ranked 2nd in task two
Ranked 2nd in task three
Abstract
In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce. We discuss our practical solution for this competition, ranking 6th in task one, 2nd in task two, and 2nd in task 3. The code is available at https://github.com/wufanyou/KDD-Cup-2022-Amazon.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · Web Data Mining and Analysis