字节跳动推荐算法实习生-电商业务
实习兼职A161372A地点:上海状态:招聘
任职要求
1、2026届硕士及以上学位在读,计算机相关专业; 2、扎实的算法和数据结构基础,优秀的编码能力; 3、机器学习基础扎实,熟悉CF、MF、FM、Word2vec、LR、GBDT、DNN、Wide&Deep等常用的算法模型; 4、熟悉下任何一个开源工具:Xgboost、Tensorflow、Pytorch; 5、熟悉C++和Python语言…
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工作职责
ByteIntern:面向2026届毕业生(2025年9月-2026年8月期间毕业),为符合岗位要求的同学提供转正机会。 团队介绍:Data-电商团队,负责电商创新项目的算法和大数据工作。依托于字节跳动产品,帮助用户发现并获得好物,享受美好生活。在这个团队,我们不仅要通过推荐和搜索算法帮助用户买到感兴趣的好东西,也要通过风控算法和智能平台治理算法去甄别违规行为,保护用户的购物体验;我们还要建设智能客服技术、大规模商品知识图谱来提升各个交易环节的效率;我们也要结合机器学习和运筹算法,来优化供应链和物流的效率和成本,并进一步提升用户体验;另外我们还会用人工智能来帮助商家提升经营能力。我们的使命:没有难卖的优价好物,让美好生活触手可得。 1、参与千万~亿级规模的电商个性化推荐算法的优化:包括抖音/今日头条/番茄小说等产品线的商品推荐、直播推荐、短视频推荐等; 2、通过表征学习、图模型、深度学习、迁移学习、多任务学习等技术提升信息匹配的效率,让每个用户可以便捷的找到优质好货; 3、挖掘和分析海量用户行为数据,进行用户长短期兴趣建模,以及潜在兴趣预测,提升推荐的精准性; 4、通过算法自动挖掘优质、专业、高口碑的商品和主播,构建良性的循环机制,优化内容电商生态; 5、结合内容电商的业务特性,进行模型和算法创新,打造业界领先的推荐算法和系统。
包括英文材料
学历+
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/
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
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.
GBDT+
https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt
Like bagging and boosting, gradient boosting is a methodology applied on top of another machine learning algorithm.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
XGBoost+
[英文] What is XGBoost?
https://www.ibm.com/think/topics/xgboost
XGBoost (eXtreme Gradient Boosting) is a distributed, open-source machine learning library that uses gradient boosted decision trees, a supervised learning boosting algorithm that makes use of gradient descent.
https://www.youtube.com/watch?v=BJXt-WdeJJo
takes a deep dive into one of the most powerful machine learning algorithm, eXtreme Gradient Boosting, using a Jupyter notebook with Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
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