快手推荐算法工程师-【海外Push】
社招全职D4870地点:北京状态:招聘
任职要求
1、有扎实的数据结构和算法功底,熟悉业界主流的机器学习算法,掌握基本的数据挖掘; 2、有Push、推荐系统、搜索引擎、自然语言处理相关相关经验者优先; 3、具备强悍的编码能力,熟悉Linux开发环境,熟悉至少一门编程语言; 4、较好的团队合作精神,较强的沟通能力;主动性强,有很强的自我驱动力; 5、有良好的产品意识、敏锐的数据业务分析能力,对解决挑战性问题充满热情,致力于将事情做到120分。 加分项: 1、有推荐系统、机器学习、自然语言理解相关领域研究或者实习经验者优先; 2、在SIGKDD、ICML、RECSYS、NIPS等国际顶级会议上有论文发表者优先; 3、有ICPC、Topcoder Algorithm或类似算法竞赛经历者优先; 4、有因果推断,Uplifting model相关建模经验者优先。
工作职责
1、负责快手国际化Push相关的算法研发、优化工作,运用策略和算法手段促进用户增长; 2、负责Push推荐系统的搭建以及相关算法落地,面对亿级别的用户群体情况下实现Push的个性化匹配,做到千人千面; 3、负责Push的算法、策略的设计,并直接参与Push场景下推荐系统的全链路开发与优化,包括但不局限于触发、召回、粗排、精排、下发策略等阶段; 4、从海量数据中挖掘用户消费行为、社交关系网以及运营热点实现Push内容池的搭建。
包括英文材料
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
数据挖掘+
https://www.youtube.com/watch?v=-bSkREem8dM
Database vs Data Warehouse vs Data Lake
https://www.youtube.com/watch?v=7rs0i-9nOjo
推荐系统+
[英文] Recommender Systems
https://www.d2l.ai/chapter_recommender-systems/index.html
Recommender systems are widely employed in industry and are ubiquitous in our daily lives.
NLP+
https://www.youtube.com/watch?v=fNxaJsNG3-s&list=PLQY2H8rRoyvzDbLUZkbudP-MFQZwNmU4S
Welcome to Zero to Hero for Natural Language Processing using TensorFlow!
https://www.youtube.com/watch?v=R-AG4-qZs1A&list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
Natural Language Processing tutorial for beginners series in Python.
https://www.youtube.com/watch?v=rmVRLeJRkl4&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4
The foundations of the effective modern methods for deep learning applied to NLP.
Linux+
https://ryanstutorials.net/linuxtutorial/
Ok, so you want to learn how to use the Bash command line interface (terminal) on Unix/Linux.
https://ubuntu.com/tutorials/command-line-for-beginners
The Linux command line is a text interface to your computer.
https://www.youtube.com/watch?v=6WatcfENsOU
In this Linux crash course, you will learn the fundamental skills and tools you need to become a proficient Linux system administrator.
https://www.youtube.com/watch?v=v392lEyM29A
Never fear the command line again, make it fear you.
https://www.youtube.com/watch?v=ZtqBQ68cfJc
ICML+
https://icml.cc/
RecSys+
[英文] Recommender Systems
https://recsys.acm.org/
This site contains information about the ACM Recommender Systems community, the annual ACM RecSys conferences, and more.
因果推断+
https://web.stanford.edu/~swager/causal_inf_book.pdf
How best to understand and characterize causality is an age-old question in philosophy.
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