Pinterest engineering blog

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  • May 15, 2014
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Understanding usage patterns with data

Andrea Burbank

Andrea is a data engineer at Pinterest.

One of the most exciting aspects of working with Pinterest data is its opportunity to connect people with things and ideas they’re interested in. We know that interests change over time, and even day to day. What you’re interested in on Sunday morning when you want an awesome pancake recipe may not align exactly with the travel plans you’re dreaming up on Saturday.

Since one of our goals is to help Pinners find the content that inspires them at any moment, we’re constantly asking ourselves how we can help people discover the things they care about by making the right recommendations at the optimal time. Our answer lies in the data infrastructure we’ve built.

Digging into Pin Trends

We recently looked at aggregate data to see which categories peak throughout the week and which interests were most popular among Pinners at various times.

What we found is that TGIF is real. People start the week off motivated and Pinning mostly to fitness boards on Mondays, technology is popular on Tuesdays, and inspirational quotes see a spike on Wednesdays as people work through hump day. Fashion is big on Thursdays, while people are ready for a laugh on Friday and humor Pins spike. Over the weekend, travel is the top category on Saturday, and the week closes out on Sunday with food and craft ideas.

Improving discovery with context

As new content is created on Pinterest, we can identify the context behind a Pin based on a mix of signals, such as the board in which the Pin was added. Just knowing when an individual Pin is created might not give us too much information on its own, but because hundreds of others may have saved a similar Pin, we can deduce what that Pin is about. With a timestamp for that action, we can track how popular different categories of Pins are at different times of day or across the days of the week.

We can go a level deeper by looking at the context of an action, such as if it was discovered in home feed, category feed, or search. We can use this information to make the product easier to navigate, as well as to build a more relevant recommendation engine.

Using these different sources, we did the analysis of Pinners’ propensity to engage with different topics by time of day, day of week, and month of the year. Learn more about these Pin trends on our Pinner Blog. If you’re interested in digging into this type of data, join our team!

Andrea Burbank is a data engineer at Pinterest.

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