Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising

TL;DR

This paper introduces MC-SBN, a deep learning model that effectively encodes multi-channel user behaviors into dynamic vectors, improving ad relevance and prediction performance in native advertising without user queries.

Contribution

The paper proposes a novel multi-channel RNN-based user encoder with attention that can be incrementally updated for large-scale native advertising applications.

Findings

Improves ad relevance ranking in native advertising.

Enhances click and conversion prediction accuracy.

Demonstrates scalability with real-world datasets.

Abstract

Multiple content providers rely on native advertisement for revenue by placing ads within the organic content of their pages. We refer to this setting as ``queryless'' to differentiate from search advertisement where a user submits a search query and gets back related ads. Understanding user intent is critical because relevant ads improve user experience and increase the likelihood of delivering clicks that have value to our advertisers. This paper presents Multi-Channel Sequential Behavior Network (MC-SBN), a deep learning approach for embedding users and ads in a semantic space in which relevance can be evaluated. Our proposed user encoder architecture summarizes user activities from multiple input channels--such as previous search queries, visited pages, or clicked ads--into a user vector. It uses multiple RNNs to encode sequences of event sessions from the different channels and then applies an attention mechanism to create the user representation. A key property of our approach is that user vectors can be maintained and updated incrementally, which makes it feasible to be deployed for large-scale serving. We conduct extensive experiments on real-world datasets. The results demonstrate that MC-SBN can improve the ranking of relevant ads and boost the performance of both click prediction and conversion prediction in the queryless native advertising setting.