  Many recent advances in neural information retrieval models, which predict
top-K items given a query, learn directly from a large training set of (query,
item) pairs. However, they are often insufficient when there are many
previously unseen (query, item) combinations, often referred to as the cold
start problem. Furthermore, the search system can be biased towards items that
are frequently shown to a query previously, also known as the 'rich get richer'
(a.k.a. feedback loop) problem. In light of these problems, we observed that
most online content platforms have both a search and a recommender system that,
while having heterogeneous input spaces, can be connected through their common
output item space and a shared semantic representation. In this paper, we
propose a new Zero-Shot Heterogeneous Transfer Learning framework that
transfers learned knowledge from the recommender system component to improve
the search component of a content platform. First, it learns representations of
items and their natural-language features by predicting (item, item)
correlation graphs derived from the recommender system as an auxiliary task.
Then, the learned representations are transferred to solve the target search
retrieval task, performing query-to-item prediction without having seen any
(query, item) pairs in training. We conduct online and offline experiments on
one of the world's largest search and recommender systems from Google, and
present the results and lessons learned. We demonstrate that the proposed
approach can achieve high performance on offline search retrieval tasks, and
more importantly, achieved significant improvements on relevance and user
interactions over the highly-optimized production system in online experiments.
