Seq2Slate: Re-ranking and Slate Optimization with RNNs

TL;DR

Seq2Slate introduces a sequence-to-sequence RNN model for ranking that captures item interactions and optimizes entire slates, improving relevance and appeal in information retrieval and recommendation systems.

Contribution

The paper presents a novel RNN-based re-ranking model that directly optimizes entire item slates, capturing complex item interactions in a scalable, end-to-end trainable framework.

Findings

Effective in standard ranking benchmarks

Improves user engagement in real-world recommendation systems

Captures complex item dependencies in slate ranking

Abstract

Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be placed alongside it. In this work, we propose a sequence-to-sequence model for ranking called seq2slate. At each step, the model predicts the next `best' item to place on the slate given the items already selected. The sequential nature of the model allows complex dependencies between the items to be captured directly in a flexible and scalable way. We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data. We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.