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having built, and partnered with other, marketplaces, i can appreciate the product ambitions but am also a bit skeptical. in my experience the matching optimiza

having built, and partnered with other, marketplaces, i can appreciate the product ambitions but am also a bit skeptical. in my experience the matching optimization problem is idiosyncratic (per market segment), and is likely beyond machine learning capabilities[0] to deliver long-term advantage, though perhaps enough short-term advantage is delivered to create a business. [0]: note that google and facebook try to solve this problem broadly by seeing more and more of your behavior and trying to better infer intent with essentially unlimited resources, and basically fail at it.

andrewyates2020 5y ago
Thank you! We actively seek out customers with idiosyncratic matching because we're better at it than alternatives. We rely on user engagement, in-session model responsiveness, and in-house expertise from the marketplaces themselves. Part of the way we solve this is NOT with machine learning, but with tools to empower internal merchandizing teams and product teams in a way that fits nicely with the automated system. If you're a on a search team and had to goof around with elastic-search scores or hack in inserts for a new market merchandizing team, you've felt this pain. The path forward is ML + human expertise, which is better than either alone. > basically fail at it Our goal is to figure out "why" and "how to make it better." These are $T companies and dominate all performance ad spend. It's hard to think about such big numbers. One problem is that they start with crappy inventory (people who want to advertise) and it's really hard to actually _do_ something on these platforms with promotions that you do see. On marketplaces, you don't have these problems as much, because everything is already vetted and you can convert in the marketplace. That's why you're there, so it's a great experience. So, we start from there, media matching that people love, and work backwards.

andrewyates2020 5y ago

Thank you! We actively seek out customers with idiosyncratic matching because we're better at it than alternatives. We rely on user engagement, in-session model responsiveness, and in-house expertise from the marketplaces themselves. Part of the way we solve this is NOT with machine learning, but with tools to empower internal merchandizing teams and product teams in a way that fits nicely with the automated system. If you're a on a search team and had to goof around with elastic-search scores or hack in inserts for a new market merchandizing team, you've felt this pain. The path forward is ML + human expertise, which is better than either alone. > basically fail at it Our goal is to figure out "why" and "how to make it better." These are $T companies and dominate all performance ad spend. It's hard to think about such big numbers. One problem is that they start with crappy inventory (people who want to advertise) and it's really hard to actually _do_ something on these platforms with promotions that you do see. On marketplaces, you don't have these problems as much, because everything is already vetted and you can convert in the marketplace. That's why you're there, so it's a great experience. So, we start from there, media matching that people love, and work backwards.