Clover's starred repositories
SummaryOfLoanSuspension
全国各省市停贷通知汇总
the-craft-of-selfteaching
One has no future if one couldn't teach themself.
DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
pdf2htmlEX
Convert PDF to HTML without losing text or format.
easytrader
提供同花顺客户端/国金/华泰客户端/雪球的基金、股票自动程序化交易以及自动打新，支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件
twinkle-tray
Easily manage the brightness of your monitors in Windows from the system tray
pyalgotrade
Python Algorithmic Trading Library
Data-Structure
《数据结构》-严蔚敏.吴伟民-教材源码与习题解析
time-as-a-friend
《把时间当作朋友》
Play-with-Data-Structures
Codes of my MOOC Course <Play Data Structures in Java>. Updated contents and practices are also included. 我在慕课网上的课程《Java语言玩转数据结构》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
artificial-intelligence-for-trading
Content for Udacity's AI in Trading NanoDegree.
Knowledge-Graph-Tutorials-and-Papers
Insightful Tutorials and Papers about Knowledge Graphs
ratel-core
平头哥的核心代码
awesome-mybatis-plus
🎉 A curated list of awesome things related to MyBatis-Plus
DeltaTrader
极简版Python量化交易工具
sa-sdk-java
神策数据官方 Java 埋点 SDK，是一款轻量级用于 Java 端的数据采集埋点 SDK。
cos-java-sdk-v5
java sdk for qcloud cos v5 (xml api)
wxcloudrun-springboot
微信云托管 springboot 框架模版
seedlabs-chinese
Chinese translation of the SEED Labs
imooc-python-494
程序员理财课 Python量化交易系统实战
EBSN
Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms for users to plan, arrange, and publish events, have gained increasing popularity and rapid growth. EBSNs capture not only the online social relationship, but also the offline interactions from offline events. They contain rich heterogeneous information, including multiple types of entities, such as users, events, groups and tags, and their interaction relations. Three recommendation tasks, namely recommending groups to users, recommending tags to groups, and recommending events to users, have been explored in three separate studies. However, none of the proposed methods can handle all the three recommendation tasks. In this paper, we propose a general graph-based model, called HeteRS, to solve the three recommendation problems on EBSNs in one framework. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities. Experimental results on two real-world datasets demonstrate that our proposed method significantly outperforms the state-of-the-art methods for all the three recommendation tasks, and the learned influence weights help understanding user behaviors.