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Many recent advances in neural information retrieval models, which predicttop-K items given a query, learn directly from a large training set of (query,item) pairs. However, they are often insufficient when there are manypreviously unseen (query, item) combinations, often referred to as the coldstart problem. Furthermore, the search system can be biased towards items thatare 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 thatmost online content platforms have both a search and a recommender system that,while having heterogeneous input spaces, can be connected through their commonoutput item space and a shared semantic representation. In this paper, wepropose a new Zero-Shot Heterogeneous Transfer Learning framework thattransfers learned knowledge from the recommender system component to improvethe search component of a content platform. First, it learns representations ofitems 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 searchretrieval task, performing query-to-item prediction without having seen any(query, item) pairs in training. We conduct online and offline experiments onone of the world's largest search and recommender systems from Google, andpresent the results and lessons learned. We demonstrate that the proposedapproach can achieve high performance on offline search retrieval tasks, andmore importantly, achieved significant improvements on relevance and userinteractions over the highly-optimized production system in online experiments.

Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval