Wednesday, Aug 9 & Thursday, Aug 10, 2023 | 8:00 AM - 11:00 AM PDT
View course GitHub page haossr/icme-recommendation-system-summer-2023
We increasingly rely on personal and professional advice generated and curated by algorithms, rather than friends and family.
In the first session of this workshop, we start by discussing the historical context of search and recommendation systems. Next, we delve into the mechanisms behind these systems, which heavily rely on state-of-the-art machine learning techniques. The process involves converting implicit and explicit user preferences into structured data that can be computationally analyzed to provide valuable recommendations. Additionally, we examine how search can be seen as a specialized form of recommendation.
In the second session of the workshop, we focus on the efficient implementation of these computations using contemporary distributed computing methods.
Hao Sheng is a Staff Machine Learning Engineer at Apple SPG (Special Projects Group). He received his PhD in Computer Engineering from ICME, Stanford University as part of Stanford Machine Learning Group and Stanford Computational Policy Lab. His research interests are machine learning algorithms for environmental and social sustainability. He worked on large-scale recommendation systems at TikTok before joining Apple. Prior to Stanford, he earned his Bachelors in PPE (Philosophy, Political Science, and Economics) and a B.S. in Applied Mathematics from Yuanpei College, Peking University.
Rui Yan is a Ph.D. candidate at ICME, Stanford. Her research focuses on developing machine learning algorithms to extract meaningful representations from large-scale, high-dimensional and multi-modal data. She has interned at Meta, Uber AI, and Microsoft Research.
The Zoom link to the course can be found here.
This advanced workshop is designed for experienced Python programmers, preferably with recent experience applying neural networks.
Familiarity with deep learning at the level of SWS 11: Introduction to Deep Learning, is expected.
Day I: Recommendation Systems 101
Zoom recording Day 1: Link
Day II: Advanced Topics
Zoom recording Day 2: Link
To join the workshop, you’ll need a device with a recent web browser and two-way audio and video access to Zoom. This could be a laptop or desktop computer running any operating system, such as Windows, Mac, or Linux. Participative activities benefit from a larger screen, so joining via a smartphone or tablet may not provide the best learning experience.
You do not need to install Python or any other software before the workshop. We will provide more detailed instructions prior to the start to ensure that you are set up and ready to learn.
Our goal is to create an inclusive and supportive learning environment, and we want all students to succeed. However, to set you up for success, we also want to clearly communicate the necessary level of prior knowledge and programming skill. If you are unsure whether you have the required background, please feel free to reach out for guidance.
Workshop attendees will be positioned to leverage their newfound expertise in search and recommendation systems to contribute to industry practices and make informed decisions on the implications of these increasingly prevalent technologies.
Specifically, attendees will:
A list of references at varying levels of sophistication are provided later in this description. Students interested in maximizing their learnings from this workshop will do well to familiarize themselves with as many of the references as possible.