US 8,429,027 B2Granted

Mobile-based real-time food-and-beverage recommendation system

AssigneeYahoo! Inc.

InventorZhaohui Zheng

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Abstract

Particular embodiments extract a plurality of users, a plurality of establishments, and a plurality of items from dining information provided by at least one of the plurality of users, each of the plurality of establishments sells food or beverage; construct a user-establishment matrix, a user-item matrix, and an establishment-item matrix using the plurality of users, the plurality of establishments, and the plurality of items; generate a user latent representation for the plurality of users, an establishment latent representation for the plurality of establishments, and an item latent representation for the plurality of items; and compute one or more correlations using the user latent representation, the establishment latent representation, or the item latent representation, wherein each of the one or more correlations is between two users, two establishments, two items, one user and one establishment, one of user and one item, or one establishment and one item.

Claims17

Dependent claims

A method comprising:

by one or more computing devices,
extracting a plurality of users, a plurality of establishments, and a plurality of items from dining information provided by at least one of the plurality of users, wherein:
each of the plurality of establishments sells food or beverage and has been visited by at least one of the plurality of users;
and
each of the plurality of items is food or beverage sold at least one of the plurality of establishments and has been ordered by at least one of the plurality of users at least one of the plurality of establishments;
constructing a user-establishment matrix, a user-item matrix, and an establishment-item matrix using the plurality of users, the plurality of establishments, and the plurality of items;
generating a user latent representation for the plurality of users, an establishment latent representation for the plurality of establishments, and an item latent representation for the plurality of items;
and
computing one or more correlations using the user latent representation, the establishment latent representation, or the item latent representation, wherein each of the one or more correlations is between two of the plurality of users, two of the plurality of establishments, two of the plurality of items, one of the plurality of users and one of the plurality of establishments, one of the plurality of users and one of the plurality of items, or one of the plurality of establishments and one of the plurality of items;
and
receiving a recommendation request from a mobile device associated with a first one of the plurality of users at a first one of the plurality of establishment;
determining one or more item recommendations with respect to the first user and the first establishment using the one or more correlations;
and transmitting the one or more item recommendations to the mobile device.

10 more elements

A system comprising:

a memory comprising instructions executable by one or more processors;
and the one or more processors coupled to the memory and operable to execute the instructions, the one or more processors being operable when executing the instructions to:
extract a plurality of users, a plurality of establishments, and a plurality of items from dining information provided by at least one of the plurality of users, wherein:
each of the plurality of establishments sells food or beverage and has been visited by at least one of the plurality of users;
and
each of the plurality of items is food or beverage sold at least one of the plurality of establishments and has been ordered by at least one of the plurality of users at least one of the plurality of establishments;
construct a user-establishment matrix, a user-item matrix, and an establishment-item matrix using the plurality of users, the plurality of establishments, and the plurality of items;
generate a user latent representation for the plurality of users, an establishment latent representation for the plurality of establishments, and an item latent representation for the plurality of items;
and
compute one or more correlations using the user latent representation, the establishment latent representation, or the item latent representation, wherein each of the one or more correlations is between two of the plurality of users, two of the plurality of establishments, two of the plurality of items, one of the plurality of users and one of the plurality of establishments, one of the plurality of users and one of the plurality of items, or one of the plurality of establishments and one of the plurality of items;
and
receive a recommendation request from a mobile device associated with a first one of the plurality of users at a first one of the plurality of establishment;
determine one or more item recommendations with respect to the first user and the first establishment using the one or more correlations;
and
transmit the one or more item recommendations to the mobile device.

12 more elements

One or more non-transitory computer-readable storage media embodying software operable when executed by one or more computer systems to:

extract a plurality of users, a plurality of establishments, and a plurality of items from dining information provided by at least one of the plurality of users, wherein:
each of the plurality of establishments sells food or beverage and has been visited by at least one of the plurality of users;
and
each of the plurality of items is food or beverage sold at least one of the plurality of establishments and has been ordered by at least one of the plurality of users at least one of the plurality of establishments;
construct a user-establishment matrix, a user-item matrix, and an establishment-item matrix using the plurality of users, the plurality of establishments, and the plurality of items;
generate a user latent representation for the plurality of users, an establishment latent representation for the plurality of establishments, and an item latent representation for the plurality of items;
and
compute one or more correlations using the user latent representation, the establishment latent representation, or the item latent representation, wherein each of the one or more correlations is between two of the plurality of users, two of the plurality of establishments, two of the plurality of items, one of the plurality of users and one of the plurality of establishments, one of the plurality of users and one of the plurality of items, or one of the plurality of establishments and one of the plurality of items;
and
receive a recommendation request from a mobile device associated with a first one of the plurality of users at a first one of the plurality of establishment;
determine one or more item recommendations with respect to the first user and the first establishment using the one or more correlations;
and
transmit the one or more item recommendations to the mobile device.

10 more elements

Dependencies are inferred from the claim text.

Facts

Application: 12/941,842Filed 8 Nov 2010
Published: US 2012/0116915 A110 May 2012
Granted: 23 Apr 2013
Term ends: Not estimatedThe source records don’t support an estimate.

Classification3 codes

G06Q 30/02
Commerce: marketing; Price estimation or determination; Fundraising
G06Q 10/42
Business processes related to social networking or social networking services: determination of affinities or common interests between users
G06Q
Information and communication technology [ict] specially adapted for administrative, commercial, financial, managerial