Algorithm-Practice-in-Industry
Introduction to the Repository
The "Algorithm-Practice-in-Industry" project initially served as a repository for collecting articles on industrial practices in search, recommendation, advertising, and other related fields. These articles were sourced from platforms like Zhihu, Datafuntalk, and various technical blogs.
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Resource Collection: The repository primarily focuses on gathering articles from major companies in the industry. It includes links to articles shared by leading practitioners in search, recommendation, and advertising technologies. This effort aims to provide a comprehensive collection of insights and practices from well-known firms. It should be noted that the content is merely a compilation of resources, and specific contents are not directly cited.
Users can explore the articles through a link provided here, and they are all organized in a master file called source.xlsx, which can be sorted as desired by users.
Expansion into Other Areas
Beyond the initial articles, the repository has expanded to include several interesting features:
- Paper Push Bot: This feature leverages GitHub Action to automatically push new papers related to search and recommendation from platforms like Arxiv to a Feishu group.
- Conference Paper Listings: A detailed list of top conference papers related to search and recommendation is maintained, covering years from 2012 to 2024 for conferences like CIKM, ECIR, KDD, RECYS, SIGIR, WSDM, and WWW.
- Quality Blog Articles on Search and Recommendation: High-quality articles from notable bloggers are compiled, providing diverse perspectives and innovative approaches in the field.
Contributing New Articles
The project encourages contributions by allowing users to submit issues. An automatic process using GitHub Actions updates the repository's README and the source file with new content. To facilitate contributions, an issue template is available for guidance.
Communication and Collaboration
The creator of this project, a 2023 graduate initially focused on Natural Language Processing (NLP), has since shifted to search and recommendation roles. They are now working particularly in the sector of recall systems and are enthusiastic about adapting to this dynamic field.
For anyone interested in the creator's reading notes, they are hosted openly on their GitHub page. Communication and collaboration are welcomed, and the user’s contact information is available for those seeking further interaction.
The Paper Push Bot
- Arxiv Paper Updates: By using GitHub Actions along with translation and automation tools like CAYun XiaoYi and Feishu robots, this feature ensures that new papers related to cs.IR and cs.CL are shared daily with a group on Feishu. This allows for effortless skimming of the latest research for those with limited time.
- Top Conference Papers: Similarly, daily updates from top conferences related to search and recommendation fields are automated to keep users informed of trends and breakthroughs.
List of Conference Papers
The repository provides a structured list of conference papers spanning several years across notable conferences. This comprehensive list allows researchers and practitioners easy access to historic and current academic work that has shaped the field of search, recommendation, and related technologies.
Industrial Practice Articles
A significant section of the repository is dedicated to articles by major industry players, offering insights into practical implementations and innovations deployed in the field. Companies like Tencent, Alibaba, ByteDance, and Meituan have shared their experiences and advancements in algorithms applied in real-world industrial applications.
This collection not only highlights technological advancements but also facilitates learning from various applied scenarios, bringing real-world insight to individuals interested in technical and strategic developments in the industry.
By compiling these resources and facilitating an active community of contributors, the Algorithm-Practice-in-Industry project provides a valuable bridge between academic research and practical application in the fast-evolving fields of search, recommendation, and advertising algorithms.