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.

Recommender systems have become ubiquitous, transforming user interactions with products, services and content in a wide variety of domains. In content recommendation, recommenders generally surface relevant and/or novel personalized content based on learned models of user preferences (e.g., as in collaborative filtering [Breese et al., 1998, Konstan et al., 1997, Srebro et al., 2004, Salakhutdinov and Mnih, 2007]) or predictive models of user responses to specific recommendations. Well-known applications of recommender systems include video recommendations on YouTube [Covington et al., 2016], movie recommendations on Netflix [Gomez-Uribe and Hunt, 2016] and playlist construction on Spotify [Jacobson et al., 2016]. It is increasingly common to train deep neural networks (DNNs) [van den Oord et al., 2013, Wang et al., 2015, Covington et al., 2016, Cheng et al., 2016] to predict user responses (e.g., click-through rates, content engagement, ratings, likes) to generate, score and serve candidate recommendations. Practical recommender systems largely focus on myopic prediction--estimating a user's immediate response to a recommendation--without considering the long-term impact on subsequent user behavior. This can be limiting: modeling a recommendation's stochastic impact on the future affords opportunities to trade off user engagement in the near-term for longer-term benefit (e.g., by probing a user's interests, or improving satisfaction).

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.