Google Inc.

Sagar Jain

Lichan Hong

Jilin Chen

Ed Huai-Hsin Chi

Irwan Bello

Ofer Pinhas Meshi

Sayali Satish Kulkarni

Amy Xian Zhang

Systems and methods for generating a slate of ranked items are provided. In one example embodiment, a computer-implemented method includes inputting a sequence of candidate items into a machine-learned model, and obtaining, in response to inputting the sequence of candidate items into the machine-learned model, an output of the machine-learned model that includes a ranking of the candidate items that presents a diverse set of the candidate items at the top positions in the ranking such that one or more highly relevant candidate items can be demoted in the ranking.

Systems and methods for slate optimization with recurrent neural networks

Implementations provide an improved system for presenting search results based on entity associations of the search items. An example method includes, for each of a plurality of crowdsource workers, initiating display of a first randomly selected cluster set from a plurality of cluster sets to the crowdsource worker. Each cluster set represents a different clustering algorithm applied to a set of search items responsive to a query. The method also includes receiving cluster ratings for the first cluster set from the crowdsource worker and calculating a cluster set score for the first cluster set based on the cluster ratings. This is repeated for remaining cluster sets in the plurality of cluster sets. The method also includes storing a cluster set definition for a highest scoring cluster set, associating the cluster set definition with the query, and using the definition to display search items responsive to the query.

Crowdsourced evaluation and refinement of search clusters

Growing community of inventors

Mountain View, CA, United States of America

Sagar Jain