AutoTask: Task Aware Multi-Faceted Single Model for Multi-Task Ads Relevance

TL;DR

AutoTask introduces a multi-faceted attention model with task-aware features and cross-task interactions, significantly improving relevance prediction across diverse ad scenarios with a unified approach.

Contribution

The paper presents a novel auto-regressive attention-based model with task ID encoding for multi-task ad relevance, enhancing generality and performance over existing models.

Findings

Outperforms generalized DNN models in relevance accuracy

Effectively handles multiple ad scenarios with a single model

Improves generality for unseen tasks

Abstract

Ads relevance models are crucial in determining the relevance between user search queries and ad offers, often framed as a classification problem. The complexity of modeling increases significantly with multiple ad types and varying scenarios that exhibit both similarities and differences. In this work, we introduce a novel multi-faceted attention model that performs task aware feature combination and cross task interaction modeling. Our technique formulates the feature combination problem as "language" modeling with auto-regressive attentions across both feature and task dimensions. Specifically, we introduce a new dimension of task ID encoding for task representations, thereby enabling precise relevance modeling across diverse ad scenarios with substantial improvement in generality capability for unseen tasks. We demonstrate that our model not only effectively handles the increased computational and maintenance demands as scenarios proliferate, but also outperforms generalized DNN models and even task-specific models across a spectrum of ad applications using a single unified model.