This repository serves as a curated collection of industry-practice articles and research papers focused on search, recommendation, and advertising algorithms. It aims to provide practitioners and researchers with insights into real-world applications and advancements in these fields.

How It Works

The project aggregates content from various sources, including technical blogs, forums, and conference proceedings. It categorizes these resources by application area (search, recommendation, advertising, user growth) and company, allowing users to easily find relevant information. The repository also includes automated bots for pushing new research papers and conference lists.

Quick Start & Requirements

This repository is a collection of links and summaries, not a software package. No installation or specific requirements are needed to access the information.

Highlighted Details

• Extensive collection of industry practice articles from major tech companies like Alibaba, Tencent, Meituan, and ByteDance.
• Covers a wide range of topics including recall, ranking, cold-start, multi-task learning, graph neural networks, and causal inference.
• Includes a bot that pushes new Arxiv and top conference papers in search, recommendation, and advertising.
• Maintains lists of top conference papers and influential bloggers in the field.

Maintenance & Community

The repository is maintained by a single contributor, a 2023 graduate transitioning into the search, recommendation, and advertising domain. Community interaction is encouraged through GitHub issues for contributions.

Licensing & Compatibility

The repository itself does not specify a license. The content consists of links to external articles, and users should adhere to the terms of service of the original sources.

Limitations & Caveats

The repository is a curated list and does not provide direct access to the full content of the articles; it links to external sources. Some older links might be broken or behind paywalls.