US 11,443,347 B2Granted

System and method for click-through rate prediction

AssigneeSamsung Electronics Co., Ltd.

InventorsXianshan Qu, Li Li, Xi Liu, Rui Chen, Yong Ge, Soo-Hyun Choi

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Abstract

A system and method capable of learning dynamic user and advertisement behavior for more effective click-through rate prediction. The system and method include at least one processor configured to obtain at least one item data, wherein the at least one item data comprises at least one explicit feedback to a user interaction event and other data associated with an item. The at least one processor also uses an interaction model that incorporates the obtained at least one item data to generate a user response prediction for a user and another interaction event.

Claims20

Dependent claims

A method comprising:

obtaining, by an electronic device, at least one item data, wherein the at least one item data comprises (i) at least one explicit feedback to a user interaction event and (ii) historical advertisement data comprising one or more bid requests;
identifying, by the electronic device, at least one external data source;
aligning, by the electronic device, the at least one item data to the at least one external data source based on at least one contextual data, creating pairs of aligned data, wherein for each of the pairs of the aligned data, aligning the at least one item data comprises:
computing a weighted linear combination of a temporal interest of a user and a previous temporal interest of the user;
and
passing the weighted linear combination as an input to a next cell of an interaction model;
generating, by the electronic device, an aggregate result based on passing the weighted linear combinations through a series of cells of the interaction model;
and
generating, by the electronic device using the interaction model, a user response prediction for the user and another interaction event.

6 more elements

An electronic device comprising:

at least one memory;
and
at least one processor operatively coupled to the at least one memory, the at least one processor configured to:
obtain at least one item data, wherein the at least one item data comprises (i) at least one explicit feedback to a user interaction event and (ii) historical advertisement data comprising one or more bid requests;
identify at least one external data source;
align the at least one item data to the at least one external data source based on at least one contextual data, creating pairs of aligned data, wherein for each of the pairs of the aligned data, the at least one processor is configured to:
compute a weighted linear combination of a temporal interest of a user and a previous temporal interest of the user;
and
pass the weighted linear combination as an input to a next cell of an interaction model;
generate an aggregate result based on passing the weighted linear combinations through a series of cells of the interaction model;
and
generate, using the interaction model, a user response prediction for the user and another interaction event.

9 more elements

A non-transitory machine-readable medium comprising instructions that, when executed by at least one processor of an electronic device, cause the at least one processor to:

obtain at least one item data, wherein the at least one item data comprises (i) at least one explicit feedback to a user interaction event and (ii) historical advertisement data comprising one or more bid requests;
identify at least one external data source;
align the at least one item data to the at least one external data source based on at least one contextual data, creating pairs of aligned data, wherein for each of the pairs of the aligned data, the at least one processor is configured to:
compute a weighted linear combination of a temporal interest of a user and a previous temporal interest of the user;
and
pass the weighted linear combination as an input to a next cell of an interaction model;
generate an aggregate result based on passing the weighted linear combinations through a series of cells of the interaction model;
and
generate, using the interaction model, a user response prediction for the user and another interaction event.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/006,495Filed 28 Aug 2020 · us provisional application
Priority: 30 Aug 2019
Published: US 2021/0065251 A14 Mar 2021
Granted: 13 Sep 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification12 codes

G06Q 30/0275
Determination of fees for advertising: auctions
G06N 3/044
Architecture, e.g. interconnection topology: recurrent networks, e.g. Hopfield networks
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/0499
Architecture, e.g. interconnection topology: feedforward networks

Family

Application62/894,681Us provisional application · filed 30 Aug 2019
US 2021/0065251 A1Pre-grant publication · published 4 Mar 2021
US 11,443,347 B2This patent

From the US filing history. Foreign family members aren’t listed.

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