Introduction to the FunRec Project
FunRec is a comprehensive tutorial aimed at those with a foundation in machine learning who are seeking positions in recommendation algorithm roles. The tutorial is designed to guide learners from the basics of recommendation algorithms to hands-on practices and even interview preparation. The project is divided into four main sections: Overview of Recommendation Systems, Algorithm Basics, Hands-on Practice, and Interview Preparation for Algorithm Engineers. Each section is well-detailed and structured to facilitate a thorough understanding for beginners.
Overview of Recommendation Systems
This section provides a broad summary of recommendation systems, explaining their significance and applications. It also covers the architecture and the related technology stacks essential for building recommendation systems. The aim is to offer beginners a clear picture of what recommendation systems entail.
Algorithm Basics in Recommendation Systems
Here, the project introduces foundational and crucial algorithms for algorithm engineers working in recommendation systems. It covers traditional algorithms such as recall and ranking methods. As the project evolves, more key algorithms and techniques like re-ranking and cold start will be included.
Key Topics in Algorithm Basics
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Classic Recall Models: Includes collaborative filtering models like UserCF and ItemCF, vector-based recall techniques such as FM and item2vec, as well as various tower and graph models.
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Classic Ranking Models: Discusses models like GBDT+LR and feature cross models including FM, DCN, and the Wide & Deep series.
Hands-on Practice with Recommendation Systems
This section offers practical experience, particularly through competition practice and a news recommendation system demo. The competition exercises are aligned with Alibaba's Tianchi News Recommendation entry competition. The news recommendation system practice project builds a demo with both front-end and back-end interactions, providing a holistic view of the recommendation system pipeline albeit without commercial value.
Interview Preparation for Recommendation Algorithm Engineers
Aimed at those preparing for interviews, this section consolidates foundational knowledge and popular technologies often tested in algorithm engineer interviews. It is designed to empower learners to ace interviews after gaining a solid foundation in recommendation algorithms through work experience.
Community and Contributions
FunRec is developed by a group of passionate individuals devoted to sharing knowledge. Although the contributors have limited experience, they have prepared this material with care and encourage learners to provide feedback for any errors found. The community invites suggestions for improvements and welcomes contributions.
For learning and community engagement, FunRec has established a dedicated learning community (WeChat group+Knowledge Planet). Knowledge Planet is free for students and is aimed at content retention. The community also shares technical summaries on platforms such as Bilibili.
Overall, FunRec stands out as an invaluable resource for those aspiring to excel in the field of recommendation algorithms, blending theoretical knowledge with practical insights and preparing learners for real-world applications and interviews.