  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.
