Kids Need to Read

To help underfunded communities create a culture of reading, Kids Need to Read accepts requests for books from libraries, schools, and various literacy programs through an online application. Based on the age ranges and demographics of the population served, Kids Need to Read provides select books from their growing book list of more than 350 titles. Submissions from programs serving adolescent juvenile offenders, high school dropouts, youths living in poor urban or rural communities, immigrant children, kids with learning challenges, or children living on Native American reservations are strongly encouraged.

Responsible Recommendation of Information Items: Veracity, Fairness, and Trust

Xiuzhen (Jenny) Zhang

Professor of RMIT University, Australia

Recommender systems have grown beyond E-commerce applications to novel domains. In particular, information items such as news articles, social media posts and reviews are complex items associated with properties such as author’s viewpoints, stance, reputation, credibility, and bias. We posit that the objective of recommendation of such complex information items shall go beyond high accuracy of satisfying user personal interests to high social responsibility. Socially responsible recommender systems call for shift to a new paradigm of data preparation, recommendation model and evaluation. In this talk, I will discuss our recent work on recommendation of social media items for the mitigation of misinformation and fairness of information sources, as well as user trust of information items.

Speaker Bio:
Xiuzhen (Jenny) Zhang is Professor of Data Science at RMIT University, Australia. Her research interests are data mining and machine learning, with a focus on textual data and social media data. She is especially interested in data science for social good, in areas such as misinformation detection and mitigation, law enforcement, and digital health. She has published over 100 papers in these areas. She is an associate editor of the journal Information Processing and Management, and has served on the organising committee of international and Australian conferences such as KDD, PAKDD, EMNLP, IEEE DSAA, ALTW, ADMA.

Scaling, Strengthening and Serving: Innovating through Large-Scale Deep Representations in E-commerce Product Search and Recommendation Applications

Belinda Zeng

GM, Head of Applied Science and Engineering - Amazon Search Science and AI

Recent work in e-commerce product search and recommendations have shown result improvement by using large scale pre-trained model based Deep Learning approach. However, this has not been an easy task in the real-world development and deployment due to several constraints: 1) scaling the size of model and training data with computational efficiency is challenging; 2) boosting the incumbent ML applications performance using large scale pre-trained model in the real business setting requires careful algorithm design and data strategy; and 3) serving millions of requests per second at high throughput and low latency is a daunting task. In this talk, I will share our recent work that addresses these issues and a vision for future innovations.

Speaker Bio:
Belinda Zeng is currently Head of Applied Science and Engineering in Amazon Search Science and AI. Over her career, she has been taking various leadership roles at Amazon (Alexa AI and Amazon Consumer Payments), Nielsen, Discover financial and Deloitte Consulting. She is currently leading Amazon's large scale AI program where she works with a group of top-notch scientists and engineers building universal semantic representations of e-commerce specific entities. This effort aims to bring Amazon services beyond the current state-of-the-art and unlock many new downstream applications that bring delightful experiences to Amazon customers. She obtained her PhD in Economics and Master in Mathematics in Indiana University - Bloomington.