Apple Inc.

Hon Yuk Chan

Vipul Ved Prakash

Lun Cui

Julien F Freudiger

John Martin Hörnkvist

Anubhav Malhotra

Stanley N Hung

Karolina Buchner

Vivek Sehgal

João Pedro De Almeida Forjaz De Lacerda

A method and apparatus of a device that re-rank a plurality of search results received from a search server on the device is described. In an exemplary embodiment, the device receives, with a browser of the device, a search query from a user. The device further sends the search query to a search server. In addition, the device receives the plurality of search results from the search server, wherein the plurality of search results is ranked according to a first ranking. The device also retrieves browser history of the user, wherein the browser history is collected on the device without revealing the browser history to the search server. The device further re-ranks the plurality of search results according to a second ranking based on at least the browser history. In addition, the device presents the plurality of search results using the second ranking.

Client-side search result re-ranking

A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.

Blending learning models for search support

A method and apparatus of a device that re-rank a plurality of search results is described. In an exemplary embodiment, the device receives a search query from a user and generates the plurality of search results over a plurality of search domains, wherein the plurality of search results is ranked according to a first ranking. The device additionally generates a re-ranking model, where the re-ranking model includes a plurality of intra-domain models that are generated based on at least based on-device interactions of a plurality of users interacting with a plurality of other devices and each of the plurality of search domains corresponds to one of the plurality of intra-domain models. The device further re-ranks the plurality of search results using the re-ranking model and presents the plurality of search results using the second ranking.

Re-ranking search results using blended learning models

Growing community of inventors

Santa Clara, CA, United States of America

Hon Yuk Chan