Learning to Rank Recommendations with the k-Order Statistic Loss

Making recommendations by learning to rank is becoming
an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering
datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked
list. In this work we present a family of loss functions, the korder statistic loss, that includes these previous approaches
as special cases, and also derives new ones that we show to
be useful. In particular, we present (i) a new variant that
more accurately optimizes precision at k, and (ii) a novel
procedure of optimizing the mean maximum rank, which
we hypothesize is useful to more accurately cover all of the
user’s tastes. The general approach works by sampling N
positive items, ordering them by the score assigned by the
model, and then weighting the example as a function of this
ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations,
where we obtain improvements for computable metrics, and
in the YouTube case, increased user click through and watch
duration when deployed live on www.youtube.com.

• Information Retrieval and the Web

• Machine Intelligence