Many recommendation systems rely on point-wise models, which score items
individually. However, point-wise models generating scores for a video are
unable to account for other videos being recommended in a query. Due to this,
diversity has to be introduced through the application of heuristic-based
rules, which are not able to capture user preferences, or make balanced
trade-offs in terms of diversity and item relevance. In this paper, we propose
a novel method which introduces diversity by modeling the impact of low
diversity on user's engagement on individual items, thus being able to account
for both diversity and relevance to adjust item scores. The proposed method is
designed to be easily pluggable into existing large-scale recommender systems,
while introducing minimal changes in the recommendations stack. Our models show
significant improvements in offline metrics based on the normalized cross
entropy loss compared to production point-wise models. Our approach also shows
a substantial increase of 1.7% in topline engagements coupled with a 1.5%
increase in daily active users in an A/B test with live traffic on Facebook
Watch, which translates into an increase of millions in the number of daily
active users for the product.

提出了一种通过建模低多样性对用户对个别项目的参与度影响以调整项目得分来实现多样性和相关性均衡的新方法，该方法可插入现有的大规模推荐系统，具有显著的性能提高。