Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

TL;DR

This paper introduces an unsupervised hierarchical clustering method to identify query types in email search, and develops a multi-task neural ranking model that predicts query types to improve search relevance.

Contribution

It presents a novel unsupervised query clustering technique and a multi-task neural ranking model that jointly predicts query types and ranks documents, enhancing email search performance.

Findings

Multi-task neural model outperforms baseline models.

Unsupervised query clustering effectively captures query diversity.

Significant improvement in email search ranking accuracy.

Abstract

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.