  As the use of online platforms continues to grow across all demographics,
users often express a desire to feel represented in the content. To improve
representation in search results and recommendations, we introduce end-to-end
diversification, ensuring that diverse content flows throughout the various
stages of these systems, from retrieval to ranking. We develop, experiment, and
deploy scalable diversification mechanisms in multiple production surfaces on
the Pinterest platform, including Search, Related Products, and New User
Homefeed, to improve the representation of different skin tones in beauty and
fashion content. Diversification in production systems includes three
components: identifying requests that will trigger diversification, ensuring
diverse content is retrieved from the large content corpus during the retrieval
stage, and finally, balancing the diversity-utility trade-off in a
self-adjusting manner in the ranking stage. Our approaches, which evolved from
using Strong-OR logical operator to bucketized retrieval at the retrieval stage
and from greedy re-rankers to multi-objective optimization using determinantal
point processes for the ranking stage, balances diversity and utility while
enabling fast iterations and scalable expansion to diversification over
multiple dimensions. Our experiments indicate that these approaches
significantly improve diversity metrics, with a neutral to a positive impact on
utility metrics and improved user satisfaction, both qualitatively and
quantitatively, in production.
  An accessible PDF of this article is available at
https://drive.google.com/file/d/1p5PkqC-sdtX19Y_IAjZCtiSxSEX1IP3q/view
