Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
This work addresses the challenge of improving recommendation accuracy for users in systems with implicit feedback, representing an incremental advancement in collaborative filtering techniques.
The paper tackles the problem of learning user preferences from implicit feedback in recommendation systems by proposing a novel ranking framework that minimizes pairwise ranking loss and jointly learns user and item embeddings. The approach demonstrates competitive performance with state-of-the-art methods on real-world benchmarks.
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.