ShusenTang/BDC2019
2019中国高校计算机大赛——大数据挑战赛 第三名解决方案
This project offers a solution for predicting the click-through rate (CTR) of documents in search results. It takes anonymized search query and document title data as input and outputs a prediction of whether a user will click on a specific document for a given query. This is designed for search engineers, data scientists, or product managers working on improving search relevance and user engagement.
122 stars. No commits in the last 6 months.
Use this if you need a strong baseline or reference implementation for predicting search document click-through rates based on query-title matching.
Not ideal if you are looking for a plug-and-play solution without any technical development work, as this is a code-based competition entry.
Stars
122
Forks
25
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 16, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ShusenTang/BDC2019"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
IliaLarchenko/behavior-1k-solution
1st place solution of 2025 BEHAVIOR Challenge
fire717/hualubei2020-callingsmoking
2020中国华录杯·数据湖算法大赛—定向算法赛（吸烟打电话检测）决赛第二名开源
aasu14/Data-Science-Hackathon-And-Competition
Grandmaster in MachineHack (3rd Rank Best) | Top 70 in AnalyticsVidya & Zindi | Expert at Kaggle...
rbouadjenek/deakin-ai-challenge2023
The Deakin Simpsons challenge 2023 is a computer vision competition that is designed to provide...
seculayer/AutoAPE-challenge2
Kaggle 2차년도(2021)