Learning to Rank Recommendations with the k-Order Statistic Loss
Venue
ACM International Conference on Recommender Systems (RecSys) (2013)
Publication Year
2013
Authors
Jason Weston, Hector Yee, Ron Weiss
BibTeX
Abstract
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.
