Search

As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts., Comment: Accepted in KDD 2021

Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with the combinatorics of the RL action space. We develop SlateQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates. Under mild assumptions on user choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs. We demonstrate our methods in simulation, and validate the scalability and effectiveness of decomposed TD-learning on YouTube.

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.

Wait-free implementations of shared objects tolerate the failure of processes, but not the failure of base objects from which they are implemented. We consider the problem of implementing shared objects that tolerate the failure of both processes and base objects.We identify two classes of object failures:responsiveandnonresponsive. With responsive failures, a faulty object responds to every operation, but its responses may be incorrect. With nonresponsive failures, a faulty object may also “hang” without responding. In each class, we definecrash, omission,andarbitrarymodes of failure.We show that all responsive failure modes can be tolerated. More precisely, for all responsive failure modes ℱ, object typesT, andt≥ 0, we show how to implement a shared object of typeTwhich ist-tolerant for ℱ. Such an object remains correct and wait-free even if up totbase objects fail according to ℱ. In contrast to responsive failures, we show that even the most benign non-responsive failure mode cannot be tolerated. We also show that randomization can be used to circumvent this impossibility result.Graceful degradationis a desirable property of fault-tolerant implementations: the implemented object never fails more severely than the base objects it is derived from, even if all the base objects fail. For several failure modes, we show wheter this property can be achieved, and, if so, how.

The publish/subscribe (or pub/sub) paradigm is an increasingly popular model for interconnecting applications in a distributed environment. Many existing pub/sub systems are based on pre-defined subjects, and hence are able to exploit multicast technologies to provide scalability and availability. An emerging alternative to subject-based systems, known as content-based systems, allow information consumers to request events based on the content of published events. This model is considerably more flexible than subject-based pub/sub. However, it was previously not known how to efficiently multicast published events to interested content-based subscribers within a large and geographically distributed network of broker (or router) machines. We develop and evaluate a novel and efficient distributed algorithm for this purpose, called -link matching". Link matching performs just enough computation at each node to determine the subset of links to which an event should be forwarded. We show via simulations that: link matching yields higher throughput than flooding when subscriptions are selective; and the overall CPU utilization of link matching is comparable to that of centralized matching.

With the emergence of the internet, independent applications are starting to be integrated with each other. This has created a need for technology for glueing together applications both within and across organizations, without having to re-engineer individual components. We propose an approach for developing this glue technology based on message flows and discuss the open research problems in realizing this approach.