Lee Fan, an engineering executive at Pinterest, outlines internal machine learning experiments aimed at tailoring product and style recommendations based on user characteristics.
“We have some experiment in-house. If you take a picture of yourself, and we learn your skin tone. But by the way, we can also learn from the pins you like, and we know there's certain style, certain shape of a model you like to see.”
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More from Lee Fan
AssertionNot checkable as stated
Lee Fan: Individual engineer throughput can differ by 10x to 100x
“Meaning that engineer versus engineer, the throughput can be 10 X or even hundred X difference.”
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Opinion
Lee Fan: Engineering compensation should reflect individual productivity differences
“I also believe that we should reward according to it.”
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“In some way, this is different from Google. There's no right or wrong.”
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Lee Fan: Products should expand discovery before driving conversion based on intent
“What we could do as a product is expand your horizon and help you to discover new, interesting ideas. We don't have to push you into deep, say, purchase this or book this. But then later, we can tell users' behavior. They're going narrow and narrow. Now we, I …”
Lee FanJan 2, 2019▶ 9:26a16z Podcast | Engineering Intent
Insight
Lee Fan: Computer vision progress in subjective domains is limited by data
“Right now, I will say a lot of domain is limited by the data. If you only have a limited data to teach, let's say fashion, how can we know this is a fashion that are high end and more for the runway instead of a daily? It's a lot of data because it is a subtle…”
Lee FanJan 2, 2019▶ 16:10a16z Podcast | Engineering Intent
AssertionNot checkable as stated
Lee Fan: Pinterest trains AI to identify visual cues that make photos inspirational
“We are trained computer to learn why this image of the same living room, you take a picture of this way and that way, that looks so different. One just looks so inspirational. The other, like maybe just boring and the computer will start to learn those cues an…”
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