Pinterest Turns to Deep Learning to Generate Related Pins

When it comes to generating Related Pins, board co-occurrence is out and deep learning is in.

When it comes to generating Related Pins, board co-occurrence is out and deep learning is in.

Kevin Ma, a software engineer on Pinterest’s Discovery team, detailed the change in a blog post, explaining the logic behind the switch as follows:

One of the most popular ways people find ideas on Pinterest is through Related Pins, an item-to-item recommendations system that uses collaborative filtering. Previously, candidates were generated using board co-occurrence–signals from all the boards a Pin is saved to. Now, for the first time, we’re applying deep learning to make Related Pins even more relevant.

Ultimately, we developed a scalable system that evolves with our product and people’s interests, so we can surface the most relevant recommendations through Related Pins. In this post, we’ll cover how we use deep learning to generate recommendation candidates, which, in testing, has increased engagement with Related Pins by 5 percent.

Ma also highlighted some of the issues with using board co-occurrence to generate Related Pins:

  • Board segmentation: Pinners often have multiple boards for one interest. Someone may save a wine-related idea to a wine board, but cocktails made with wine to a separate cocktails board. This gap in boards makes it challenging to recommend related drinks for the wine Pin.
  • Board granularity: Boards are usually created for a broad topic, so Related Pins surfaced using board co-occurrence can be tangentially related. Figure 2 shows a query Pin of a lion couple cuddling, which was saved to the boards “animals” and “wild animals.” As a result, the Related Pins are not exactly cuddling lions, but all kinds of wild animals.
  • Board drifting: The topic of a board tends to change over time as an interest evolves. For example, a “healthy” board could start with fitness ideas and eventually evolve into other areas like recipes.

Board co-occurrence can lose the context of a Pin, so we needed a way to better understand Pins and their relative relationships to Pinners. Inspired by the Word2vec approach for creating word embeddings in the context of human language, we developed Pin2vec to embed Pins in the context of Pinners’ activity.

For a thorough explanation of how Pin2vec works, please see Ma’s blog post.

Readers: What are your thoughts on this change by Pinterest?

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