The very thing that makes the internet so useful to so many people - the vast quantity of information that's out there - can also make going online frustrating.
There's so much available that the sheer volume of choices can be overwhelming. That's where recommender systems come in, explains NVIDIA AI Podcast host Noah Kravitz.
To dig into how recommender systems work - and why these systems are being harnessed by companies in industries around the globe - Kravitz spoke to Even Oldridge, senior manager for the Merlin team at NVIDIA.
Some highlights, below. For the full conversation we, um, recommend you tune in to the podcast.
The AI Podcast Recommender Systems 101: NVIDIA's Even Oldridge Breaks It Down Ep. 164
Question: So what's a recommender system and why are they important? Oldridge: Recommender systems are ubiquitous, and they're a huge part of the internet and of most mobile apps, and really, most places have interaction that a person has with a computer. A recommender system, at its heart, is a system for taking the vast amount of options available in the world and boiling them down to something that's relevant to the user in that time or in that context.
That's a really significant challenge, both from the engineering side and the systems and the models that need to be built. Recommender systems, in my mind, are one of the most complex and significant machine learning challenges of our day. You're trying to represent what a user like a real live human person is interested in at any given moment. And that's not an easy thing to do, especially when that person may or may not know what they want.
Question: So broadly speaking, how would you define a recommender system? Oldridge: A recommender system is a sort of machine learning algorithm that filters content. So you can query the recommender system to narrow down the possible options within a particular context. The classic view that most people have with recommender systems is online shopping, where you're browsing for a particular item, and you're seeing other items that are potentially useful in that same context or similar content. And, with sites like Netflix and Spotify and content distributors, you're seeing content based on the content that you've viewed in the past. The recommender system's role is to try and build a summary of your interests and try to come up with the next relevant thing to show you.
Question: In these different examples that you talked about, do they generally operate the same way across different shopping sites or content sites that users might go to? Or are there different ways of approaching the problem? Oldridge: There are patterns to the problem, but it's one of the more fragmented industries, I think. If you look at things like computer vision or natural language processing, there are open-source datasets that have allowed for a lot of significant advancements in the field and allowed for standardization and benchmarking, and those fields have become pretty standardized because of that. In the recommender system space, the interaction data that your users are generating is part of the core value of your company, so most companies are reticent to reveal that data. So there aren't a lot of great public recommender system datasets out there.
Question: What are you doing at NVIDIA? How did NVIDIA get into the business of recommender systems? What role does your team play? Oldridge: Why NVIDIA is interested in the business of recommender systems, is, to quote Jensen [Huang, NVIDIA's CEO], that recommender systems are the most important algorithm on the internet, and they drive a lot of the financial and compute decisions that are being made. For NVIDIA, it's a very interesting machine learning workload that I think previously has been more applicable to the CPU or has been done more on the CPU.
We've gotten to a place where recommender systems on GPUs make a ton of sense. There aren't many people who are trying to run large natural language processing models or large computer vision models on a CPU. For recsys, there's a lot of people still focused on the CPU-based solutions. There's a strong motivation for us to get this right because we have a vested interest in selling GPUs, but beyond that, there's a similar degree of acceleration that's possible that led to the revolutions that have happened in computer vision and NLP. When things can happen 10 times faster, then you're able to do much more exploration and much more diving into the problem space. The field begins to take off in a way that it hasn't before, and that's something that our team is really focused on: How we can enable these teams to develop recommender systems much more quickly and efficiently, both from the compute time perspective and making sure that you can develop features and train models and deploy to production really quickly and easily.
Question: How do you judge the effectiveness of a recommender system? Oldridge: There's a wide variety of factors that are used to determine and compare the effectiveness, both offline when you're developing the model and trying to evaluate its performance, and online when you're running the model in production and the model serving the customer. There's a lot of metrics in that space. I don't think there's any one clear answer of what you need to measure. I think a lot of different companies are, at the heart of it, trying to measure longer-term user engagement, but that's a very lagging signal. So you need to tie it to metrics that are much more immediate, like interaction and clicks on items, etc.
Question: Could one way to judge it potentially be something like the number of Amazon purchases a user ends up returning? Oldridge: I think many
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The very thing that makes the internet so useful to so many people - the vast quantity of information that's out there - can also make going online frustrating.
