JP5632124B2 - 格付け方法、検索結果並び替え方法、格付けシステム及び検索結果並び替えシステム - Google Patents
格付け方法、検索結果並び替え方法、格付けシステム及び検索結果並び替えシステム Download PDFInfo
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Description
のユーザから直接入力された情報を用いるフィードバックメカニズムを用いて、検索関連性メソッド及び重みを調整することが望ましい。
高いかを判定する際、異なる検索モード及びユーザの興味の違いを考慮できる検索エンジンの開発が望まれる。
メントを含む第3の結果リストを返すステップとを有する。この結果、本発明の実施の形態は、1又は複数の検索エンジンによって返された結果を用いて機能できる。式は、好ましくは、ユーザ入力を用いて判定される。
識別子、及びドキュメントのユーザ格付けのうちの1つ以上を含む。
又は他の如何なる文字を含んでいてもよい。
U2iPod」等、関連する検索のためのクエリタームを表示する。領域140は、例えば、「バンドU2」の概念のための「ボノ(Bono)」又は「偵察機U2」の概念のための「ドラゴンレディ(Dragon Lady)」等、ユーザによって入力され、又はアルゴリズムから導出された、他のクエリタームに関連する概念への「ここも参照(see also)」ハイパーリンクを含む。
rites)」等の他のタームで表される領域190のアイコンをクリックすることによって行われる。ユーザが異なれば、どのサイトが関連性が高いかに関する結論も異なるので、本発明に基づくアルゴリズムがリストされたサイトの順序を決定してもよい。一実施の形態においては、アルゴリズムは、「多数決」処理を用いて、最も多くの「票」が投票された(例えば、最も多くのユーザによって「セーブ」された)ドキュメントを結果リストの上位に表示する。
明示的に評価していないことを意味する。これは、データセットが、純粋に、ユーザによって格付けされたドキュメントのみから構成されるという意味で重要である。
る。そして、データポイントは、何らかの確立された期間又は何らかのある最大データセットサイズに戻るシステム内の全ての格付けイベントデータポイントから構成される更新データベースに加えられる。疎ロジスティック回帰(sparse logistic regression)演算は、数百万個のデータポイントに対して実行できる。この制限に達すると、最後のN個のデータポイントに対して、回帰演算が実行される。ここで、Nは、数百万以内の数と予測することができる。推定されるモデルは、以下の式に基づき、与えられたクエリ−ドキュメントの対について、ユーザ格付けイベント毎に特定される。
判定する。1つ以上の停止基準が満たされていない場合、処理は、ステップ430に戻り、この他の場合、処理は、ステップ480に進み、包括的モデル及びユーザモデルが更新される。
数実行された場合、(2)交差検定に失敗した場合(例えば、テストデータがトライアルデータと異なっていた場合)、(3)ソリューションが修飾した場合、すなわち、先の実行におけるパラメータと、次の実行におけるパラメータとの間の差分が、所定の値を下回る場合等がある。
Claims (5)
- 検索クエリに応じてユーザに提示されたドキュメントの結果リスト内のドキュメントを格付けする格付け方法において、
a.ユーザクライアントによって、上記ドキュメントの結果リストをユーザに提示するステップと、
b.検索エンジンによって、上記ドキュメントの結果リスト内のドキュメントのユーザに対する関連性に関するユーザからの入力を、受信するとともに、上記検索クエリに関連したユーザによる最新の入力と、該ユーザに対するドキュメントの関連性に関するドキュメントのみを記憶するステップと、
c.上記検索エンジンによって、上記ユーザ入力を上記検索クエリ及び上記ドキュメントに関連させるステップと、
d.機械学習モジュールによって、複数の特徴からなる変数と、該複数の特徴のそれぞれに対応する複数の重み係数からなるパラメータとを有し、上記ドキュメントと上記検索クエリとの関連性スコアを計算する関連式を決定するステップと、
e.上記検索エンジンによって、上記関連性スコアに基づいてドキュメントを格付けするステップとを有し、上記関連性スコアは、上記ドキュメント及び検索クエリに関連したユーザ入力に依存することを特徴とする格付け方法。 - 検索クエリに応じて返された検索結果リストを並び替える検索結果並び替え方法において、
a.ユーザクライアントによって、上記検索クエリに応じた検索結果リストをユーザに提示するステップと、
b.検索エンジンによって、上記検索結果リスト内の複数の結果のそれぞれのユーザに対する関連性であるユーザからの入力を、受信するとともに、該複数の結果のそれぞれに関連したユーザによる最新の入力と、該検索結果リスト内の複数の結果のそれぞれのユーザに対する関連性に関する検索クエリのみを記憶するステップと、
c.上記検索エンジンによって、上記ユーザ入力をその結果及びドキュメントに関連させるステップと、
d.機械学習モジュールによって、複数の特徴からなる変数と、該複数の特徴のそれぞれに対応した複数の重み係数からなるパラメータとを含み、上記複数の結果毎の関連性スコアを決定する関連式を、該複数の結果毎のユーザ入力を用いて更新するステップと、
e.上記検索エンジンによって、複数の結果を含む検索結果リストを並び替えるステップとを有し、
上記複数の結果は、該複数の結果毎の関連性スコアを用いて格付けされることを特徴とする検索結果並び替え方法。 - 第1の検索エンジンによってユーザクライアントのユーザに返される結果を格付けする格付け方法において、
a.ウェブサーバによって、検索クエリに応じて、上記第1の検索エンジンによって格付けされたドキュメントを含む第1の結果リストを受信するステップと、
b.上記ウェブサーバによって、上記検索クエリに応じて、第2の検索エンジンによって格付けされたドキュメントを含む第2の結果リストを受信するステップと、
c.ユーザクライアントによって、上記第1の結果リストをユーザに提示するステップと、
d.上記第1の検索エンジンによって、上記第1の結果リスト内のドキュメントのユーザに対する関連性に関するユーザからの入力を、受信するとともに、上記ドキュメントに関連したユーザによる最新の入力と、該ユーザに対するドキュメントの関連性に関する検索クエリのみを記憶するステップと、
e.上記第1の検索エンジンによって、上記ユーザ入力を上記ドキュメント及び上記検索クエリに関連させるステップと、
f.機械学習モジュールによって、複数の特徴をからなる変数と、該複数の特徴のそれぞれに対応する複数の重み係数からなるパラメータとを有し、上記検索クエリに応じて、ドキュメントの関連性スコアを決定する式を決定するステップと、
g.上記第1の検索エンジンによって、上記検索クエリに応じて、各ドキュメント毎の関連性スコアを用いて格付けされた上記第1の結果リスト及び上記第2の結果リスト内のドキュメントを含む第3の結果リストを並び替えるステップとを有し、
上記式は、上記ユーザ入力と、該ユーザ入力の上記ドキュメント及び上記検索クエリとの関連性とを用いて、決定されることを特徴とする格付け方法。 - 検索クエリに応じて結果の格付けされたリストを返す格付けシステムにおいて、
a.第1の検索クエリに応じて返された複数の結果から結果を格付けする最新のユーザ入力のみを、該複数の結果からの結果を検索クエリに関連させるとともに、ユーザに対する結果の関連性に関するユーザ入力に関連させる記録として、保存する第1のデータベースと、
b.上記第1のデータベースに接続され、複数の特徴からなる変数と、該複数の特徴のそれぞれに対応する複数の重み係数からなるパラメータとを有し、該第1のデータベースに保存され、上記第1の検索クエリに関連されるとともに、該第1の検索クエリに応じて返された複数の結果に関連されたユーザ入力を用いて、第2の検索クエリに応じて返された複数の結果から各結果毎の関連性スコアを計算する関連性モデルとを備え、
上記複数の結果から結果を上記検索クエリに関連させる記録として、上記ユーザによって以前に上記第1のデータベースに保存された入力は、破棄されることを特徴とする格付けシステム。 - 検索クエリに応じて返された検索結果リスト内の複数の結果を並び替える検索結果並び替えシステムにおいて、
a.複数の結果におけるドキュメントのユーザに対する関連性を示し、最新のユーザ入力のみを記憶し、複数の特徴からなる変数と、該複数の特徴のそれぞれに対応する複数の重み係数からなるパラメータとを有し、ドキュメントと検索クエリに関連させる、ユーザによるユーザ入力を用いて、該ドキュメントと上記検索クエリとの関連性スコアを決定する関連性モデルと、
b.上記関連性モデルに接続され、上記検索クエリを受信し、上記ドキュメントと検索クエリとの関連性スコアに基づいて格付けされた結果を含む検索結果リストを返す検索エンジンと、
c.上記検索エンジン及び上記関連性モデルに接続され、上記ドキュメントと検索クエリとの関連性スコアを決定する関連性モデルによって用いられる特徴のセットを保存するデータベースとを備えることを特徴とする検索結果並び替えシステム。
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TWI351619B (en) | 2011-11-01 |
EP1866738A2 (en) | 2007-12-19 |
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