JP5662446B2 - リアルタイム広告入札に対して競争評価モデルを使用するための学習システム - Google Patents
リアルタイム広告入札に対して競争評価モデルを使用するための学習システム Download PDFInfo
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Description
本出願は、本明細書にその全内容が引用により組み込まれている「広告の配信のためのリアルタイム入札システム」という名称の2009年8月14日出願の出願番号第61/234,186号による本出願人所有の米国特許仮出願の利益を請求する。
104 広告主
108 広告ネットワーク
110 広告取引所
112 提供者
114 分析プラットフォームユニット
Claims (8)
- 広告の切り換えにおいてリアルタイムの広告入札を行なうコンピュータシステム上で実行される方法であって、
広告の切り換えにおいて、広告を利用可能な配置に掲載する要求の受信に応答して、複数の競争経済評価モデルを配備する処理であって、各競争経済評価モデルが、その配置に関係する履歴及びリアルタイムデータを含む対応データセットを用いながら、その配置での各広告掲載に対する経済評価を予測する当該処理と、
前記複数の競争経済評価モデルの各々によって生成された各経済評価を査定し、複数の経済評価のうち最適評価をその配置での各広告掲載の現在評価として選択する処理と、
選択された前記最適評価に対応する広告を掲載したいというエンティティの望みのため、前記利用可能な配置に対する推奨の入札額を選択して前記広告の切り換えへ提示する処理であって、前記推奨の入札額は少なくとも前記現在評価及び前記配置に対する利用可能な資金が存在するかに基づいている当該処理と、
を含む方法。 - 前記競争経済評価モデルの少なくとも1つは、リアルタイムイベントデータ、履歴イベントデータ、ユーザデータ、第三者商業データ、状況データ、広告主データ、広告代理店データ、履歴広告パフォーマンスデータ、又はマシーン学習のいずれかの少なくとも一部に基づくことを特徴とする、請求項1に記載の方法。
- 前記第三者商業データは、過去にWebサイト上に掲載された広告回数に対する財務データを含むことを特徴とする請求項2に記載の方法。
- 広告の切り換えにおいてリアルタイムの広告入札を行なうコンピュータシステム上で実行される方法であって、
広告を掲載する要求の受信に応答して、複数の競争経済評価モデルを配備する処理であって、掲載できる可能性のある配置に関係する履歴及びリアルタイムデータを含む対応データセットを用いながら、掲載するための配置と広告との複数の組合せの各々に対する各経済評価をそれぞれに予測する処理と、
前記複数の競争経済評価モデルの各々によって生成された各経済評価を査定し、掲載するための配置と広告の対応する組合せの第1の評価として複数の経済評価のうち最も高い評価を選択する処理と、
前記複数の競争経済評価モデルの各々によって生成された各経済評価を再査定し、前記掲載するための配置と前記広告との前記対応する組合せに対する修正評価として1つの経済評価を選択する処理であって、該修正評価が、前記第1の評価を選択する時には利用可能でなかったリアルタイムイベントデータを使用する競争経済評価モデルの分析に少なくとも一部基づく当該処理と、
前記第1の評価を、前記掲載するための配置と前記広告との前記対応する組合せに対して推薦の入札額を導出する際に使用する第2の修正評価で置換する処理であって、前記推奨の入札額は、前記広告を掲載したいというエンティティの望みのために前記広告の切り換えへ提示され、且つ、少なくとも前記現在評価及び前記配置に対する利用可能な資金が存在するかに基づいている当該処理と、
を含む方法。 - 前記要求は、前記広告を掲載したいという望みがあるエンティティから受信され、
前記推薦の入札額に相当する入札はパブリッシャーに自動的に送信される、ことを特徴とする請求項4に記載の方法。 - 前記推薦の入札額は、広告をどのくらいの期間掲載するかに関する推薦時間に基づいている、ことを特徴とする請求項4又は5に記載の方法。
- 前記推薦の入札額は、更に、リアルタイム入札マシーンに関連付けられたリアルタイム入札ログの分析によって導出される、ことを特徴とする請求項4〜6の何れか1項に記載の方法。
- 広告の切り換えにおいてリアルタイムの広告入札を行なうコンピュータシステム上で実行される方法であって、
広告の切り換えにおいて、広告を掲載する配置に対して入札するために複数のリアルタイム入札アルゴリズムを展開する処理であって、各リアルタイム入札アルゴリズムは、掲載のために利用可能な複数の配置に関連する複数の広告のために入札を提供し、且つ、リアルタイム入札ログからのデータを使用する当該処理と、
前記各リアルタイム入札アルゴリズムのために、対応の入札に関する成功度の評価、落札のために支払われた価格、実際に掲載された広告に対する広告受信者の相互作用を含む結果をトレースする処理と、
トレースされた前記結果の分析に基づき、前記複数のリアルタイム入札アルゴリズムの中から好ましい一つを選択する処理と、
広告を掲載する要求の受信に応答して、広告を掲載したいというエンティティの望みのため、前記利用可能な配置に対する推奨の入札額を選択して前記広告の切り換えへ提示する処理であって、前記推奨の入札額は前記複数のリアルタイム入札アルゴリズムの中から選択された好ましい一つ及び前記配置に対する利用可能な資金が存在するかに基づいている当該処理と、
を含む方法。
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PCT/US2010/045545 WO2011020076A2 (en) | 2009-08-14 | 2010-08-13 | Learning system for the use of competing valuation models for real-time advertisement bidding |
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
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US11476669B2 (en) * | 2019-06-11 | 2022-10-18 | Tata Consultancy Services Limited | Method and system for building reinforcement learning (RL) based model for generating bids |
US11295347B1 (en) * | 2021-01-30 | 2022-04-05 | Walmart Apollo, Llc | Systems and methods for forecasting campaign parameters using machine learning architectures and techniques |
US20220245679A1 (en) * | 2021-01-30 | 2022-08-04 | Walmart Apollo, Llc | Systems and methods for forecasting campaign parameters using machine learning architectures and techniques |
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EP2465086A2 (en) | 2012-06-20 |
US20110040611A1 (en) | 2011-02-17 |
CN102576436A (zh) | 2012-07-11 |
US20110040636A1 (en) | 2011-02-17 |
EP2465086A4 (en) | 2015-06-10 |
JP2013502018A (ja) | 2013-01-17 |
US20110040612A1 (en) | 2011-02-17 |
US20110040635A1 (en) | 2011-02-17 |
WO2011020076A3 (en) | 2011-04-28 |
WO2011020076A2 (en) | 2011-02-17 |
JP2015097094A (ja) | 2015-05-21 |
BR112012003318A2 (pt) | 2019-09-24 |
US20110040613A1 (en) | 2011-02-17 |
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