  Online reviews are often our first port of call when considering products and
purchases online. When evaluating a potential purchase, we may have a specific
query in mind, e.g. `will this baby seat fit in the overhead compartment of a
747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer
such questions we must either wade through huge volumes of consumer reviews
hoping to find one that is relevant, or otherwise pose our question directly to
the community via a Q/A system.
  In this paper we hope to fuse these two paradigms: given a large volume of
previously answered queries about products, we hope to automatically learn
whether a review of a product is relevant to a given query. We formulate this
as a machine learning problem using a mixture-of-experts-type framework---here
each review is an `expert' that gets to vote on the response to a particular
query; simultaneously we learn a relevance function such that `relevant'
reviews are those that vote correctly. At test time this learned relevance
function allows us to surface reviews that are relevant to new queries
on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million
questions (and answers) and 13 million reviews. We show quantitatively that it
is effective at addressing both binary and open-ended queries, and
qualitatively that it surfaces reviews that human evaluators consider to be
relevant.
