Title: Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems,
Abstract: In this paper, we propose a novel ranking framework for collaborative
filtering with the overall aim of learning user preferences over items by
minimizing a pairwise ranking loss. We show the minimization problem involves
dependent random variables and provide a theoretical analysis by proving the
consistency of the empirical risk minimization in the worst case where all
users choose a minimal number of positive and negative items. We further derive
a Neural-Network model that jointly learns a new representation of users and
items in an embedded space as well as the preference relation of users over the
pairs of items. 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. The
proposed model is by nature suitable for implicit feedback and involves the
estimation of only very few parameters. Through extensive experiments on
several real-world benchmarks on implicit data, we show the interest of
learning the preference and the embedding simultaneously when compared to
learning those separately. We also demonstrate that our approach is very
competitive with the best state-of-the-art collaborative filtering techniques
proposed for implicit feedback.