Track: Art of Relevancy and Recommendations


Day of week:

Surfacing relevant content has been an active area of development for decades, and has become more important over time with the prevalence of smaller screens and, more recently, the rise of conversational interfaces. In this track we'll cover practical real-world approaches to the problem of relevancy, ranking, and recommendations.

We'll explore the ways companies are using machine learning techniques to help users find the most interesting stories, recommend products, improve search rankings and control chatbots. We hope you'll learn some new tricks and feel empowered to add more intelligence to your products. You will find the practical application of Artificial Intelligence (AI) and Deep Learning techniques in the track.

Track Host:
Toby Segaran
Lead Software Engineer @Reddit
Toby Segaran is the author of the very popular Programming Collective Intelligence book. He was the founder of Incellico, a biotech software company later acquired by Genstruct. He is currently Lead Software Engineer at Reddit
10:35am - 11:25am

by Clarence Chio
Security Research Engineer @ShapeSecurity

The age of artificial intelligence is upon us. Whether you know it or not, we interact with systems powered by machine learning on a daily basis. If you ever wondered how social networks, online retailers, and video streaming sites seem to know exactly what content and products you desire, this session is for you.

In this talk, we will walk you through the creation of a real-world relevance and recommendation system from scratch. We will cover the machine learning theory powering such...

11:50am - 12:40pm

by Bo Peng
Partner and Data Scientist @Datascope

When solving problems, data scientists often start from the data, run analyses, and then almost as an afterthought, think about presenting results to stakeholders. This rigid, linear approach often fails to produce useful results.

At Datascope, we adopt methodologies from the design community, iteratively improving our work to ensure that our deliverable is useful to our clients. In building an Expert Finder application for a Fortune 50 company, we adopted such methodologies not only...

1:40pm - 2:30pm

by Daniel Tunkelang
Data Scientist, Author of "Faceted Search"

Query understanding is about focusing less on the search results and more on the query itself. It's about figuring out what the searcher wants, rather than scoring and ranking results. Once you have established a query understanding mindset, your overall approach to search changes: you focus on query performance rather than ranking. In particular, you pay more attention to query suggestions, especially those generated through autocomplete.

In this talk, I'll show you what search looks...

2:55pm - 3:45pm

by Nikhil Garg
Engineering Manager @Quora

Quora is a high-quality knowledge platform used by more than 100M people every month. In this presentation, I will introduce various ML problems that are important for Quora to solve in order to keep our quality high at such a massive scale. I will also describe our approach to these problems and share some lessons from building and maintaining these system at production scale.

4:10pm - 5:00pm

Open Space
5:25pm - 6:15pm

by Mitul Tiwari
Worked on recommender systems such as People You May Know @LinkedIn

Smaller screens and conversational interfaces are now a reality. Recommendation and personalization are going to be key to make conversation interface a success. Traditionally recommendation systems are very important for web services like Amazon, Netflix, Facebook and LinkedIn for engaging users and discovering new content, products and connections.

This talk first describes how recommendation systems have evolved over time and how bots are emerging as a new form of delivering...



Monday Nov 7

Tuesday Nov 8

Wednesday Nov 9