Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems

1 code implementation • 29 Apr 2017 • Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii, Charlotte Laclau, Yury Maximov, Massih-Reza Amini

The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.

Collaborative Filtering Recommendation Systems +1