TEC Summit Tech Track

I'm curating and running the technical track at TEC Summit (http://tecsummit.com). We have a great program:

(1) Near-Realtime Webpage Recommendations “One at a time” Using Content Features

Today, information overload is a problem pertinent to most information systems being used on a daily basis, with the World Wide Web chief among them. One of the key goals of Stumbleupon, a web content recommendation platform, is to ease this overload, while empowering discovery of relevant information. Our subscription to the “one recommendation at a time” concept focuses in producing an experience of serendipity as users continue to surf the web, while giving us the flexibility to reactively make recommendations near-realtime. In this presentation we will present the challenges that need to be addressed to extract content features from a web page and making near-realtime recommendations using them. We will describe the main algorithmic approach as well as the general architecture motivating our choices of tools, languages and platforms.

Ashok Venkatesan is Senior Research Engineer at StumbleUpon Inc. He has extensively worked in the areas of Recommendation Systems, Topic Modeling, Text Mining and Machine Learning. Prior to his experience in the Industry, he completed a MS in Computer Science at Arizona State University.

(2) Learning From the Diner's Experience: Extracting Insights from OpenTable Diner Reviews

I will talk about how we are using data science to help transform OpenTable into a local dining expert who knows you very well, and can help you and others find the best dining experience wherever we travel! This entails a whole slew of tools from natural language processing, recommendation system engineering, sentiment analysis that have to work in synch to make that magical experience happen. One of our main sources of insight are the reviews left by diners on our website. In this talk, I will stress on what we are learning from our rich set of diner reviews, especially using topic modeling as a core tool. I will touch upon various possible applications of this technique that we are currently exploring in both restaurateur facing and diner facing features.

Sudeep Das is a Data Scientist at OpenTable, where his main focus is on mining reviews and restaurant data to extract actionable insights and enable a personalized dining experience. He has broad experience with NLP methods, especially topic modeling and its applications. Before moving into the Data Science space, Sudeep was an Astrophysicist (Princeton PhD, UC Berkeley postdoc) researching the properties of the early universe, and co-authored about 60 peer reviewed papers. He blogs about data science, astrophysics, and random things at http://datamusing.info/

(3) Organizing Real Estate Photo Collections with Deep Learning

Real Estate Websites like Trulia and Zillow host millions of property listings, with each listing consisting of rich textual description and images of the property. While rich in information, the discoverability of this data is limited by its unstructured nature. For Example, How do we learn if "granite countertops" is an interesting real estate term. And if it is, how can we assign it to one of the many photos associated with the property.
In this talk we detail our approach to organize Trulia's unstructured content into rich photo collections similar to http://Houzz.com or Zillow Digs, without the need of any explicit user tagging.
By leveraging the recent advances in deep learning for computer vision and nap, we first automatically construct a knowledge base of relevant real estate terms and then annotate our photo collections by fusing knowledge from a deep convolutional network for image recognition and a word embedding model.The novelty in our approach lies in our ability to scale to a large vocabulary of real estate terms without explicitly training a vision model for each one of them.

Shourabh Rawat is a senior data scientist at Trulia Inc based in San Francisco. He is an applied researcher at the intersection of machine learning, deep learning, NLP and computer vision. He received his Masters in Language Technologies from Carnegie Mellon University, Pittsburgh in 2013 where he researched on building multimodal (audio and visual) systems for detecting interesting events in Youtube videos.