qianxiamoli's starred repositories
workweixin
Go语言实现企业微信sdk（第三方服务商维度），集成了第三方应用sdk和自建应用代开发的sdk，支持一键生成新sdk代码，使用简单，扩展灵活。
gorm-paginator
gorm pagination extension
weworkapi_php
official lib of wework api
redis-go-cluster
redis cluster client implementation in Go
oneinstack-docker
在docker里跑oneinstack
2018Jdata_RepurchaseDate
A competition on jdata which is about repurchase date prediction ! Rank : 8 / 5182 !
TransCoder
Public release of the TransCoder research project https://arxiv.org/pdf/2006.03511.pdf
PHP-to-Golang
Helping PHP developers understand how Go (golang) code works.
develnext-ide
DevelNext - IDE for JPHP & PHP.
automatic-personality-prediction
[AAAI SAP 2020] Modeling Personality with Attentive Networks and Contextual Embeddings
ucs-predict
User Purchasing Prediction for JData Competition 京东用户品类下店铺的预测
Analysis-and-prediction-of-online-shoppers-purchasing-intention-using-various-algorithms-CAPSTONE
Build a predictive machine learning model that could categorize users as either, revenue generating, and non-revenue generating based on their behavior while navigating a website. In order to predict the purchasing intention of the visitor, aggregated page view data kept track during the visit along with some session is used and user information as input to machine learning algorithms. Oversampling/Undersampling and feature selection techniques are applied to improve the success rates and scalability of the models.
Predicting_Repeated_Buyers_Double11
Merchants sometimes run big promotions (e.g., discounts or cash coupons) on particular dates (e.g., Boxing-day Sales, "Black Friday" or "Double 11 (Nov 11th)”, in order to attract a large number of new buyers. Unfortunately, many of the attracted buyers are one-time deal hunters, and these promotions may have little long lasting impact on sales. What's more, Tmall.com as the creator of Chinese shopping carnival "Double 11 (Nov 11th)” is threatening by other e-commercial companies like Jingdong, Suning, which resluts in an increasingly high customer churn rate. As more and more customers involving in this shopping festival and more and more competitions appearing in the market, Tmall.com has to reinforce user loyalty to avoid customer loss. It is well known that in the field of online advertising, customer targeting is extremely challenging, especially for fresh buyers. However, with the long-term user behavior log accumulated by Tmall.com, we may be able to solve this problem using Machine learning models.
2019-datacastle-enbrands
2019 数据智能算法大赛 baseline
e_commerce
BDCI 电商用户购买行为预测
Online-Shopper-s-Purchasing-Intention
One of the most popular activities on the Web is shopping. It has much allure in it — you can shop at your leisure, anytime, and in your PJs. Literally anyone can have their pages built to display their specific goods and services and create a market for buyers to purchase their products. History of ecommerce dates back to the invention of the very old notion of "sell and buy,'' electricity, cables, computers, modems, and the Internet. E-commerce became possible in 1991 when the Internet was opened to commercial use. Since that date thousands of businesses have taken up residence at web sites. Even though E-commerce industry is experiencing perennial growth since its inception, one of the crucial problems is that most of the visitors still do not complete their online shopping process. This leads to loss of revenues for the online retailer’s. This study is done in order to provide a solution for the above mentioned problem by evaluating the actions taken by the visitors on E-commerce environment in real time and predicting the visitor’s shopping intent. The information provided by the visits of users is fed to machine learning classification algorithms to build a predicting model. In the process of refining the model and making it better to provide more insightful results, oversampling and feature selection pre-processing steps are employed. In the booming e-commerce sector, with as many as 1,06,086 e-commerce companies just in India, cut-throat competition plagues a business. Conversion rates being as low as 1 to 2%, a business must assess the sessions logged in by visitors to recognize potential customers with high purchase intent so that one can nurture the leads and/or use remarketing tools to convert them.