Doragd/Algorithm-Practice-in-Industry

What it solves

This project addresses the challenge of keeping up with the rapidly evolving fields of search, advertising, and recommendation systems (often abbreviated as "搜广推" in Chinese). It provides a centralized, automated hub for tracking industry-leading technical practices and academic research.

How it works

The repository leverages GitHub Actions to automate data collection and updates. It monitors arXiv for new papers in relevant fields (like cs.IR), uses large language models to rank, summarize, and translate them, and pushes daily updates to Feishu (Lark) groups. It also maintains a structured database of top-tier conference papers and industry technical articles, which are updated via GitHub Issues and automatically rendered into Markdown and web pages.

Who it’s for

It is designed for researchers, engineers, and students working in or interested in the search, advertising, and recommendation domains who want to stay informed about the latest industry trends and academic developments without manual searching.

Highlights

• Automated daily arXiv paper tracking with LLM-based summarization and translation.
• Comprehensive, organized lists of papers from top-tier conferences (e.g., KDD, RecSys, SIGIR, WWW).
• A curated collection of technical practice articles from major tech companies.
• Automated Feishu (Lark) notifications for daily paper updates.
• Fully automated workflow using GitHub Actions for content updates and website deployment.