From Rabbit Holes to Recommendations: Reddit’s Vishal Gupta

**SPEAKER_2** (0:02)
Why is balance an important consideration when thinking about nearly all things AI? Find out on today's episode.

**Vishal Gupta** (0:11)
I'm Vishal Gupta from Reddit, and you are listening to Me, Myself, and AI.

**Sam Ransbotham** (0:18)
Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, Professor of Analytics at Boston College. I've been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 12 seasons of podcast episodes. On each episode, corporate leaders, cutting-edge researchers, and AI policy makers join us to break down what separates AI hype from AI success. Hi, listeners. Thanks for joining us again. Our guest today is Vishal Gupta, Engineering Manager, Machine Learning at Reddit. He's helped bring cutting-edge AI products used by billions like YouTube, Google Ads, all the biggies. Vishal has deep expertise in machine learning, recommender systems, large-scale data processing. At Reddit, he's also been closely involved in the relationship between platforms like Reddit and the overall AI ecosystem, which I'm excited to learn about. Vishal, great to have you on the podcast.

**Vishal Gupta** (1:29)
Thank you for having me, Sam. Nice to be here.

**Sam Ransbotham** (1:32)
I read a stat that Reddit has a billion posts, which is hard to imagine, and like 100,000 communities in the fifth most visited website on the US, seventh in the world. These are crazy numbers. I suspect that most of our listeners know about Reddit. Can you give us a quick overview of what is Reddit?

**Vishal Gupta** (1:52)
Yeah. Reddit is a platform where we have hundreds of thousands of communities. People come to Reddit to explore their interests on these vast array of communities. One can subscribe to these communities, have a discussion on a topic, one can chime in to already happening discussion, and one can also go upward and downward stuff. Whether you are into Lord of the Rings, or whether you are into cricket, you can find something that you can relate to. Go engage with the content.

**Amar** (2:32)
That's great.

**Sam Ransbotham** (2:33)
It's interesting you mentioned cricket, because actually I'm on the record is not really believing that cricket exists. But we're not going to go into that. Let's start with how Reddit is using artificial intelligence. Can you give us a few examples?

**Vishal Gupta** (2:45)
Yeah. Reddit is using artificial intelligence in various types of ways. I will talk about two high-level ways of using AI, and then we can go deeper into those.
First use case of AI is enabling users finding the right communities they can engage with. So once users start their journey on Reddit, they will visit some of the communities that are maybe popular, or they are matching the interests they showed while signing up. But we want users to engage with niche communities that they can really belong to. So connecting users with the content. Another very important use case is when you are browsing Reddit, there is a feed. So if the feed is engaging, it's relevant to you, it is able to satisfy the content needs that you are looking for. AI is powering all of that. And now Reddit is a serious ads platform showing right ad to the right user at the right time, which is relevant to them. It is all powered by AI. That is another broader use case of AI within the company.

**Sam Ransbotham** (4:02)
Yeah, I guess my first reaction was probably pretty naive. I guess I didn't realize that there were 100,000 communities and so my guess was, I just typed the name of what I want, I would find what I want. But I guess this is a form of recommendation engine that you're doing. Maybe take a minute and explain recommendation engines for people.

**Vishal Gupta** (4:20)
Yeah, you're spot on, Sam. It's exactly a recommendation system. What a recommendation system does is that it captures short-term and long-term users' interests and provide recommendations that might be valuable to the users. Maybe, for example, user Vishal is typically interested in cricket and Lord of the Rings, but right now he is searching for what blender to buy. If there is an explicit query on the platform, we want to make sure that we are able to satisfy the information needs of the users. There is a recommendation system component that supports search as well. That is how I will define recommendation system.

**Sam Ransbotham** (5:09)
You've been working with recommendation systems for quite a while in your career. What's changed over the time you've been working with them? How are recommendation engines now different than the recommendation engines of our forefathers?