201030651 六、發明說明： 【發明所屬之技術領域】 本發明關於網路技術領域，尤指一種資訊推薦的方法 及裝置。 【先前技術】 隨著網際網路的普及，網際網路上的資訊資源呈指數 φ 膨脹，從而帶來了“資訊超載”和“資訊迷向”的問題， 用戶經常會迷失在大量的資訊空間中，無法順利找到自己 需要的資訊。因此出現了面向網際網路的資訊檢索、資訊 過濾和協同過濾等技術，例如一些電子商務推薦系統，這 些電子商務推薦系統直接與用戶交互，模擬商店銷售人員 向用戶提供商品推薦，幫助用戶找到所需要商品，從而順 利完成購買過程。目前的這些推薦系統是基於一種實例基 礎上的，也就是通過商品推薦商品，資訊推薦資訊，圈子 Φ 推薦圈子等等，但是這些推薦系統的覆蓋面不夠寬，精確 度不夠高，在日趨激烈的競爭環境下，由於以上這些問題 ，現有的推薦系統可能會導致用戶流失，從而降低了網站 的銷售額度和流覽量。 【發明內容】 有鑒於此，本發明實施例提供了一種資訊推薦的方法 ，用以提高推薦系統的精確度。 本發明實施例提供了一種資訊推薦的方法，包括： -5- 201030651 獲得用戶端的訪問資訊後，從記憶記錄中獲得與該訪 問資訊關聯的各類關聯資訊； 獲取該各類關聯資訊中含有至少兩類關聯資訊的關聯 資訊組合的出現屬性，分別根據每一種關聯資訊組合的出 現屬性計算該種關聯資訊組合與該訪問資訊的關聯度； 選擇滿足條件的關聯度對應的關聯資訊組合，將該關 聯資訊組合推薦給該用戶端。 本發明實施例提供了一種資訊推薦的裝置，包括： @ 獲取單元，用於獲得本地用戶端的訪問資訊後，從記 憶記錄中獲得與該訪問資訊關聯的各類關聯資訊； 計算單元，用於獲取該各類關聯資訊中含有至少兩類 關聯資訊的關聯資訊組合的出現屬性，分別根據每一種關 聯資訊組合的出現屬性計算該種關聯資訊組合與該訪問資 訊的關聯度； 推薦單元，用於選擇滿足條件的關聯度對應的關聯資 訊組合’將該關聯資訊組合推薦給該用戶端。 · 本發明實施例提供了一種伺服器，包括： 獲取單元，用於獲得用戶端的訪問資訊後，從記憶記 錄中獲得與該訪問資訊關聯的各類關聯資訊； 計算單元，用於獲得該各類關聯資訊中含有至少兩類 關聯資訊的關聯資訊組合的出現屬性，分別根據每一種關 聯資訊組合的出現屬性計算該種關聯資訊組合與該訪問資 訊的關聯度； 推薦單元，用於選擇滿足條件的關聯度對應的關聯資 -6- 201030651 訊組合，將該關聯資訊組合推薦給該用戶端。 本發明實施例中伺服器得到用戶端的訪問資訊後’從 記憶記錄中獲取與該訪問資訊關聯的各類關聯資訊’獲取 該各類關聯資訊中含有至少兩類關聯資訊的關聯資訊組合 的出現屬性，分別根據每一種關聯資訊組合的出現屬性計 算該種關聯資訊組合與該訪問資訊的關聯度，選擇滿足條 件的關聯度對應的關聯資訊組合，將該關聯資訊組合推薦 φ 給該用戶端，從而實現各種資訊流之間的交互和個性化推 薦，提高推薦系統的精確度。 【實施方式】 本發明實施例提供了一種資訊推薦的方法，該方法包 括：伺服器得到用戶端的訪問資訊後，從記憶記錄中獲取 與該訪問資訊關聯的各類關聯資訊，獲得該各類關聯資訊 中含有至少兩類關聯資訊的關聯資訊組合的出現屬性，分 ® 別根據每一種關聯資訊組合的出現靥性計算該種關聯資訊 組合與該訪問資訊的關聯度，選擇滿足條件的關聯度對應 的兩類關聯資訊組合，將該關聯資訊組合推薦給該用戶端 〇 參見圖1所示’本發明實施例的方法包括以下步驟： 步驟101:用戶端將訪問資訊發送給伺服器，例如用 戶端將商品資訊’或者部落格（blog)資訊發送給伺服器 。本發明實施例中的訪問資訊可以包括但不限於商品資訊 、blog資訊、圈子資訊、帖子資訊、產品資訊、資訊信息 201030651 、關鍵字資訊和廣告資訊等網際網路實體資訊中的一個或 多個。 本步驟中用戶端還可以將用戶端的用戶資訊發送給伺 服器，如用戶端用戶的註冊名返回給伺服器，伺服器可根 據返回的用戶端用戶的註冊名調取例如用戶的學歷資訊、 職業資訊、年齡資訊等用戶資訊，爲根據該用戶端的用戶 資訊進行二次挖掘做準備。 步驟102:伺服器從記憶記錄中獲取與步驟101中的訪 _ 問資訊關聯的各類關聯資訊。這裏，伺服器首先從記憶記 錄中獲取使用過該訪問資訊的用戶端的記錄，其中，使用 包括：流覽、收藏、購買、評論、處理、加入、推薦等這 些對資訊的執行動作，但不限於此。然後從該記錄中獲取 這些用戶端使用過的各類資訊，也就是這些用戶端使用過 的商品資訊、blog資訊、圈子資訊、帖子資訊、產品資訊 、資訊信息、關鍵字資訊和廣告資訊等這些類別的資訊， 從而獲得了與訪問資訊關聯的各類關聯資訊。 @ 步驟103:伺服器從步驟102獲取到各類關聯資訊中獲 取含有至少兩類關聯資訊的關聯資訊組合的出現屬性，分 別根據每一種關聯資訊組合的出現屬性計算該種關聯資訊 組合與該訪問資訊的關聯度。該出現屬性可以是出現次數 ，出現時間和出現平臺中的一個或多個。 這裏伺服器可以首先根據用戶端的應用場景在步驟 102獲取到各類關聯資訊中選擇含有至少兩類關聯資訊的 關聯資訊組合，即可以根據用戶端的應用場景在商品資訊 -8- 201030651 、blog資訊、圈子資訊、帖子資訊、產品資訊、資訊信息 、關鍵字資訊和廣告資訊等這些類別的關聯資訊中，選擇 含有兩類以上的關聯資訊的關聯資訊組合，比如，選擇含 有商品資訊和資訊信息的關聯資訊組合，或者選擇含有 blog資訊、圈子資訊和帖子資訊的關聯資訊組合。然後統 計每一種關聯資訊組合的出現屬性，最後分別根據每一種 關聯資訊的出現屬性計算該關聯資訊組合與該訪問資訊的 關聯度。 其中，可以首先獲取每一種關聯資訊的出現次數，然 後根據每一種關聯資訊組合的出現次數計算該種關聯資訊 組合與該訪問資訊的關聯度。也可以首先獲取每一種關聯 資訊的出現次數和出現時間這兩種出現屬性，然後根據每 —種關聯資訊組合的出現次數和出現時間計算該種關聯資 訊組合與該訪問資訊的關聯度。 這裏還可以根據步驟1 0 1中得到的用戶端的用戶資訊 • 進行二次挖掘，伺服器從步驟102獲取到各類關聯資訊中 獲取滿足用戶端的應用場景的含有至少兩類關聯資訊的關 聯資訊的出現屬性，比如獲取含有blog資訊、圈子資訊和 帖子資訊的每一種關聯資訊組合的出現屬性，在得到用戶 端的用戶資訊和使用過該訪問資訊的用戶端的用戶資訊之 後，根據該用戶端的資訊和該使用過該訪問資訊的用戶端 的資訊給每一種關聯資訊組合的出現屬性設置權重係數， 最後根據每一種關聯資訊組合的出現屬性以及對應的權重 係數計算該種關聯資訊組合與該訪問資訊的關聯度。 -9- 201030651 步驟104:伺服器將滿足條件的關聯度對應的關聯資 訊組合推薦給用戶端。 這裏，可以將步驟103中計算出的關聯度進行排序， 根據排序結果將一個或多個排序較高的關聯度對應的關聯 資訊組合推薦給用戶端。另外還可以將步驟103中計算$ 的關聯度與設定値進行比較，當該關聯度超過設定値時， 選擇該關聯度對應的關聯資訊組合，將該關聯資訊組合推 薦給本地用戶端。 本步驟中，還需要根據本地用戶端的應用場景，將上 述被推薦的關聯資訊組合轉換爲其對應要求的超文本格式 後發送給本地用戶端，也就是伺服器發送給本地用戶端包 括關聯資訊組合以及其對應的超文本格式。 步驟105:用戶端將接收到的關聯資訊組合顯示出來 ，也就是將步驟104中被推薦的關聯資訊組合和其對應的 超文本格式展現給用戶。 下面結合說明書附圖對本發明實施例作進一步詳細描 述。 參見圖2 ’本發明實施例中訪問資訊爲商品資訊，但 是並不能因此而認爲本發明實施例僅能用於商品資訊的推 薦’ blog資訊、圈子資訊、帖子資訊、產品資訊、資訊信 息、關鍵字資訊或廣告資訊等網際網路實體資訊均可通過 本發明實施例實現推薦。具體執行過程如下： 步驟201 :用戶通過本地用戶端訪問具有商品資訊的 網頁，比如用戶需要購買商品A。 -10- 201030651 步驟202 :本地用戶端將商品A資訊和用戶資訊發送給 伺服器。 步驟203 :伺服器接收到商品A資訊和用戶資訊後，在 記憶記錄中調取一段時間內與商品A資訊關聯的用戶，可 以是一個月內與商品A資訊關聯的用戶，也就是獲得一個 月內購買、流覽或收藏商品A的用戶，具體包括用戶Ml、 用戶M2、用戶M3、用戶M4和用戶M5。然後進一步調取 φ Ml、M2、M3、M4和M5使用過的各類資訊，例如Ml還購 買了商品B、商品C和商品D，流覽blogl、blog2和blog3， 流覽廣告1、廣告2、廣告3和廣告4，加入圈子1和圈子2。 而M2還購買了商品B、商品C和商品E，流覽blogl、blog2 和bio g4，流覽廣告1、廣告2和廣告5，發了帖子1和帖子2 。M3還流覽了商品B、商品D和商品F，流覽blogl、blog4 和blog5，加入圈子1，發了帖子1和帖子3«M4還收藏了商 品B、商品F和商品G，流覽bl〇g2，流覽資訊1、資訊2和資 • 訊3，加入圈子2，發了帖子3和帖子4。M5還購買了商品B 和商品F，流覽bl〇g2和blog6，流覽廣告4，加入圈子1和圈 子3，流覽資訊1和資訊4。上述這些各類資訊就是與商品A 資訊關聯的各類關聯資訊。本步驟伺服器還可以得到用戶 Ml、用戶M2、用戶M3、用戶M4和用戶M5的年齡資訊、 學歷資訊、職業資訊和家庭結構資訊等這些用戶資訊中的 —種或多種。 步驟204:根據用戶登錄網頁的應用平臺，選擇滿足 該應用平臺的關聯資訊組合，如果訪問的網頁只具有商品 -11 - 201030651 資訊和資訊信息這兩類資訊的組合，那麼選擇的關聯資訊 組合爲商品資訊和資訊信息的組合。如果訪問的網頁具有 商品資訊、blog資訊、廣告資訊和資訊信息等這幾類資訊 ，那麼選擇對應的關聯資訊組合爲商品資訊、blog資訊、 廣告資訊和資訊信息的組合，這裏根據用戶登錄網頁的應 用環境，選擇滿足應用環境的含有商品資訊、部落格資訊 、圈子資訊、帖子資訊、產品資訊、資訊信息、關鍵字資 訊和廣告資訊等中的至少兩類資訊的關聯資訊組合。本發 明實施例以訪問的網頁具有商品資訊和資訊信息這兩類資 訊爲例進行描述，那麼選擇對應的關聯資訊組合爲商品資 訊和資訊信息的組合。 步驟205 :統計每一種商品資訊和資訊信息的組合的 出現屬性，本發明實施例以出現次數爲例進行描述’但並 不限如此。在步驟203中’購買、流覽或收藏商品A的用戶 中，購買、流覽或收藏商品B且又流覽資訊1的有2人’即 商品B和資訊1的出現次數爲2;購買、流覽或收藏商品F且 又流覽資訊1的有2人，即商品F和資訊1的出現次數爲2; 購買、流覽或收藏商品B且又流覽資訊2的有1人’即商品B 和資訊2的出現次數爲1;購買、流覽或收藏商品B且又流 覽資訊3的有1人，即商品B和資訊3的出現次數爲1;購貿 、流覽或收藏商品B且又流覽資訊4的有1人’即商品B和資 訊4的出現次數爲1;購買、流覽或收藏商品F且又流覽資 訊2的有1人’即商品F和資訊2的出現次數爲1;購買、流 覽或收藏商品F又流覽資訊3的有1人，即商品F和資訊3的 201030651 出現次數爲1;購買、流覽或收藏商品F且又流覽資訊4的 有1人，即商品F和資訊4的出現次數爲1 ;購買、流覽或收 藏商品G且又流覽資訊1的有1人，即商品G和資訊1的出現 次數爲1 ;購買、流覽或收藏商品G且又流覽資訊2的有1人 ，即商品G和資訊2的出現次數爲1 ;購買、流覽或收藏商 品G且又流覽資訊3的有1人，即商品G和資訊3的出現次數 爲2。 φ 步驟2 0 6 :伺服器根據步驟2 0 5統計的結果，通過相應 的關聯演算法計算每一種商品資訊和資訊信息的組合與該 訪問資訊的關聯度，也就是與購買商品A的關聯度。這裏 可以採用協同過濾推薦演算法’也可以採用加入設定商業 規則的推薦演算法。商品B和資訊1的組合以及商品F和資 訊1的組合與商品的出現次數最髙，根據出現次數計算出 的關聯度爲2，那麼依次計算出商品B和資訊2的組合與商 品A的關聯度爲1，商品B和資訊3的組合與商品A的關聯度 Φ 爲1，商品B和資訊4的組合與商品A的關聯度爲1，商品F 和資訊2的組合與商品A的關聯度爲1，商品F和資訊3的組 合與商品A的關聯度爲1，商品F和資訊4的組合與商品A的 關聯度爲1。 本發明實施例還可根據用戶端返回的用戶資訊進行二 次挖掘。例如根據用戶的學歷對上述計算出的關聯度再次 進行挖掘，統計購買、流覽或收藏商品A的用戶的學歷， 發現高學歷的用戶可能會對商品B和資訊1比較感興趣，而 且本地用戶端返回的用戶資訊也是高學歷，因此可將商品 -13- 201030651 B和資訊1的出現次數的權重係數設爲1，而將商品F和資訊 1的出現次數的權重係數設爲0.6，這樣最終根據該用戶資 訊挖掘後的結果可能就會是商品B和資訊1與當前訪問的商 品A關聯度最高，關聯度爲2，而商品F和資訊1與當前訪問 的商品A的關聯度爲1.2。這樣依次對各種商品資訊和資訊 信息的組合的出現次數設置權重係數，根據商品資訊和資 訊信息的組合的出現次數以及對應的權重係數得到各種商 品資訊和資訊信息組合與商品A的關聯度。 步驟207 :將步驟206計算出來的關聯度與設定値進行 比較，當該關聯度超過設定値時，獲取該關聯度對應的關 聯資訊組合。本發明實施例中，關聯度的設定値爲1，在 步驟205中，關聯度大於1的關聯資訊組合爲商品B和資訊1 的組合以及商品F和資訊1的組合。 本發明實施例中，還可以按照關聯資訊組合與訪問資 訊的關聯度的大小進行排序，獲取設定數量N個關聯度對 應的關聯資訊組合，比如獲取排名靠前的兩組關聯資訊組 合。 步驟208 :伺服器將步驟207中獲取到的關聯資訊組合 發送給用戶端。伺服器將商品B和資訊1的組合以及商品F 和資訊1的組合轉換爲商品B和資訊1的組合以及商品F和資 訊1的組合對應要求的超文本格式，然後將該商品B和資訊 1的組合，商品F和資訊1的組合，以及對應要求的超文本 格式發送給本地用戶端。 或者，伺服器將N個關聯度對應的關聯資訊組合以及 -14- 201030651 對應要求的超文本格式發送給本地用戶端。 步驟209:本地用戶端將接收到的商品B和資訊1的組 合，商品F和資訊1的組合，以及對應要求的超文本格式展 示給用戶，或者將N個關聯度對應的關聯資訊組合以及對 應要求的超文本格式展示給用戶。 根據本發明上述方法可以構建一種資訊推薦的裝置， 位於後臺伺服器中，參見圖3，包括：獲取單元100，計算 ❿ 單元200和推薦單元3 00。 獲取單元100，用於獲得用戶端的訪問資訊後，從記 憶記錄中獲取與該訪問資訊關聯的各類關聯資訊。 計算單元200，用於獲得該各類關聯資訊中含有至少 兩類關聯資訊的關聯資訊組合的出現屬性’分別根據每一 種關聯資訊組合的出現屬性計算該種關聯資訊組合與該訪 問資訊的關聯度。 推薦單元3 00，用於選擇滿足條件的關聯度對應的關 • 聯資訊組合’將該關聯資訊組合推薦給該用戶端。 本發明實施例該裝置還包括：記憶單元。 記憶單元，用於記憶用戶端、以及每一個用戶端使用 過的各類資訊。 其中，獲取單元包括：第一獲取子單元110和第二獲 取子單元120 ° 第一獲取子單元110，用於從記憶記錄中獲取使用過 該訪問資訊的其他用戶端； 第二獲取子單元120，用於從該記憶記錄中獲取該其 -15- 201030651 他用戶端使用過的各類關聯資訊。 計算單元2 0 0還包括：選擇子單元210和統計子單元 220 〇 選擇子單元210，用於根據用戶端的應用場景在該各 類關聯資訊中選擇含有至少兩類關聯資訊的關聯資訊組合 〇 統計子單元22 0，用於統計每一種關聯資訊組合的出 現屬性。 推薦單元3 00還包括：第一選擇子單元310和第二選擇 子單元320。 第一選擇子單元310，按照該關聯度進行排序，選擇 排序較高的關聯度對應的關聯資訊組合。 第二選擇子單元3 20，用於將該關聯度與設定値進行 比較，當該關聯度超過設定値時，選擇該關聯度對應的關 聯資訊組合。 本發明實施例該裝置還包括：轉換單元和發送單元。 轉換單元，用於將該關聯資訊組合轉換爲該關聯資訊 組合對應要求的超文本格式。 發送單元，用於將該超文本格式發送給該用戶端。 當本發明實施中該裝置包括用戶資訊單元時，計算單 元還可以包括保存子單元和計算子單元。其中’ 用戶資訊單元，用於得到該用戶端的用戶資訊和使用 過該訪問資訊的用戶端的用戶資訊。 保存子單元，用於保存根據該用戶端的用戶資訊和該 -16- 201030651 流覽過該訪問資訊的用戶端的用戶資訊給每一種關聯資訊 組合的出現屬性設置的權重係數。 計算子單元，用於根據該每一種關聯資訊組合的出現 屬性以及對應的權重係數計算該種關聯資訊組合與該訪問 資訊的關聯度。 本發明實施例中伺服器得到用戶端的訪問資訊後，從 記憶記錄中獲取與該訪問資訊關聯的各類關聯資訊，獲取 φ 該各類關聯資訊中含有至少兩類關聯資訊的關聯資訊組合 的出現屬性，分別根據每一種關聯資訊組合的出現屬性計 算該種關聯資訊組合與該訪問資訊的關聯度，將滿足條件 的關聯度對應的關聯資訊組合推薦給用戶端，並且能根據 用戶端的用戶資訊進行二次挖掘，這樣能夠爲用戶推薦最 適合的資訊，根據用戶的任一輸入，輸出不同的推薦結果 ，輸入可以是一群人、一個圈子、一件商品、一個blog、 一個帖子、一個產品描述或一個資訊等，輸出結果可以是 Φ 一群人、一個圈子、一件商品、一個blog、一個帖子、一 個產品描述或一個資訊等的任意組合，能夠覆蓋網際網路 上所有的實體，從而實現各種資訊流之間的交互和個性化 推薦，提高了推薦的精確度和推薦資訊的覆蓋面，這樣也 就提高網站的銷售額和流覽量。 雖然通過實施例描繪了本發明，但本領域普通技術人 員知道，在不脫離本發明的精神和實質的情況下，就可使 本發明有許多變形和變化，本發明的範圍由所附的權利要 求來限定。 -17- 201030651 【圖式簡單說明】 圖1是本發明實施例資訊推薦的方法的流程圖； 圖2是本發明實施例資訊推薦的方法的具體流程圖； 圖3是本發明實施例資訊推薦的裝置結構圖。201030651 VI. Description of the Invention: [Technical Field] The present invention relates to the field of network technology, and more particularly to a method and apparatus for information recommendation. [Prior Art] With the popularity of the Internet, the information resources on the Internet have expanded exponentially, which has brought about "information overload" and "information confusion". Users are often lost in a large amount of information space. I can't find the information I need. Therefore, there are technologies for information retrieval, information filtering and collaborative filtering for the Internet, such as some e-commerce recommendation systems. These e-commerce recommendation systems directly interact with users, and simulate store sales personnel to provide product recommendations to users to help users find Need goods to complete the purchase process smoothly. The current recommendation system is based on an example, that is, through product recommendation products, information recommendation information, circle Φ recommendation circles, etc., but the coverage of these recommendation systems is not wide enough, the accuracy is not high enough, and the competition is becoming increasingly fierce. In the environment, due to these problems, the existing recommendation system may lead to user loss, thereby reducing the sales and browsing volume of the website. SUMMARY OF THE INVENTION In view of this, an embodiment of the present invention provides a method for information recommendation to improve the accuracy of a recommendation system. The embodiment of the present invention provides a method for information recommendation, including: -5- 201030651 After obtaining the access information of the client, obtaining related information related to the access information from the memory record; obtaining at least the related information in the related information The appearance attribute of the associated information combination of the two types of related information is calculated according to the appearance attribute of each related information combination, and the relevance degree of the related information combination and the access information is calculated according to the occurrence attribute of each related information combination; The associated information combination is recommended to the client. An embodiment of the present invention provides an apparatus for information recommendation, including: an obtaining unit, configured to obtain, after obtaining access information of a local user end, obtaining related information related to the access information from a memory record; and calculating a unit for acquiring The related information of the related information combination of the at least two types of related information includes the association degree of the related information combination and the access information according to the appearance attribute of each related information combination; the recommendation unit is used for selecting The associated information combination corresponding to the degree of relevance of the condition 'recommends the associated information combination to the client. The embodiment of the present invention provides a server, including: an obtaining unit, configured to obtain, according to the access information of the user end, various related information associated with the access information from the memory record; and a calculating unit, configured to obtain the type The associated attribute of the associated information combination having at least two types of related information in the related information, and calculating the degree of association between the related information combination and the access information according to the appearance attribute of each associated information combination; the recommending unit is configured to select a condition that satisfies the condition The associated information corresponding to the degree of relevance is recommended to the client. In the embodiment of the present invention, after obtaining the access information of the user end, the server obtains the related information related to the access information from the memory record, and obtains the appearance attribute of the associated information combination of the at least two types of related information in the related information. Calculating the degree of association between the related information combination and the access information according to the appearance attribute of each associated information combination, selecting the associated information combination corresponding to the conditional relevance degree, and recommending the associated information combination to the user end, thereby Realize the interaction and personalized recommendation between various information flows, and improve the accuracy of the recommendation system. Embodiments of the present invention provide a method for information recommendation. The method includes: after obtaining the access information of the user end, the server obtains various related information related to the access information from the memory record, and obtains the related associations. The presence attribute of the associated information combination containing at least two types of related information in the information, and the degree of association between the related information combination and the access information is calculated according to the occurrence of each associated information combination, and the relevance degree corresponding to the condition is selected. The combination of the two types of association information is recommended to the user terminal. Referring to the method of the embodiment of the present invention, the method includes the following steps: Step 101: The user sends the access information to the server, for example, the user end. Send product information or blog information to the server. The access information in the embodiment of the present invention may include, but is not limited to, one or more of the Internet information such as product information, blog information, circle information, post information, product information, information information 201030651, keyword information, and advertisement information. . In this step, the user terminal may also send the user information of the user terminal to the server, for example, the registration name of the user end user is returned to the server, and the server may retrieve, for example, the user's academic information and occupation according to the registered name of the returned user user. User information such as information and age information prepares for secondary mining based on user information of the user. Step 102: The server obtains various types of related information associated with the access information in step 101 from the memory record. Here, the server first obtains the record of the user end that has used the access information from the memory record, wherein the use of the information includes: browsing, collecting, purchasing, commenting, processing, joining, recommending, etc., but not limited to this. Then, from the record, the various types of information used by the client are obtained, that is, the product information, blog information, circle information, post information, product information, information information, keyword information, and advertisement information used by the user terminals. Information about the category, which results in various types of associated information associated with the access information. @ Step 103: The server obtains the appearance attribute of the related information combination containing at least two types of related information from the related information in step 102, and calculates the related information combination and the access according to the appearance attribute of each related information combination respectively. The degree of relevance of the information. The presence attribute can be one or more occurrences, time of occurrence, and occurrence of the platform. Here, the server may first select a combination of related information including at least two types of related information in the related information according to the application scenario of the user terminal, that is, according to the application scenario of the user end, the product information is -8-201030651, blog information, In the related information of category information such as circle information, post information, product information, information information, keyword information, and advertising information, select a combination of related information that contains two or more types of related information, for example, select an association that contains product information and information information. A combination of news, or a combination of related information that includes blog information, circle information, and post information. Then, the appearance attributes of each associated information combination are counted, and finally, the association degree of the related information combination with the access information is calculated according to the appearance attribute of each related information. The number of occurrences of each type of related information may be first obtained, and then the degree of association between the related information combination and the access information is calculated according to the number of occurrences of each associated information combination. It is also possible to first obtain the two appearance attributes of the occurrence number and the appearance time of each associated information, and then calculate the degree of association between the related information combination and the access information according to the number of occurrences and the occurrence time of each associated information combination. Here, the user may also perform secondary mining according to the user information of the user terminal obtained in step 101, and the server obtains the related information including the at least two types of related information that meets the application scenario of the user end by acquiring the related information from the step 102. An attribute is generated, for example, obtaining an appearance attribute of each associated information combination including blog information, circle information, and post information, and after obtaining the user information of the user end and the user information of the user end using the access information, according to the information of the user end and the The information of the user side that has used the access information sets a weight coefficient for the appearance attribute of each associated information combination, and finally calculates the degree of association between the related information combination and the access information according to the appearance attribute of each associated information combination and the corresponding weight coefficient. . -9- 201030651 Step 104: The server recommends the associated information combination corresponding to the conditional degree of association to the client. Here, the degree of association calculated in step 103 may be sorted, and one or more associated information combinations corresponding to the higher ranked relevance are recommended to the client according to the sorting result. In addition, the correlation degree calculated in step 103 can be compared with the setting ,. When the degree of association exceeds the setting ,, the associated information combination corresponding to the association degree is selected, and the related information combination is recommended to the local user end. In this step, the recommended association information combination needs to be converted into the corresponding hypertext format according to the application scenario of the local client, and then sent to the local user end, that is, the server sends the local user terminal to the associated information combination. And its corresponding hypertext format. Step 105: The UE displays the received association information in combination, that is, the recommended association information in step 104 and its corresponding hypertext format are presented to the user. The embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Referring to FIG. 2, the access information is the product information in the embodiment of the present invention, but the embodiment of the present invention cannot be used only for the recommendation of the product information, such as blog information, circle information, post information, product information, information information, The Internet entity information such as the keyword information or the advertisement information can be recommended by the embodiment of the present invention. The specific execution process is as follows: Step 201: The user accesses the webpage with the product information through the local user terminal, for example, the user needs to purchase the merchandise A. -10- 201030651 Step 202: The local client sends the product A information and user information to the server. Step 203: After receiving the product A information and the user information, the server retrieves the user associated with the product A information in the memory record for a period of time, and may be the user associated with the product A information within one month, that is, obtain one month. The user who purchases, browses, or collects the item A specifically includes the user M1, the user M2, the user M3, the user M4, and the user M5. Then further retrieve the various information used by φ Ml, M2, M3, M4 and M5, for example, Ml also purchased product B, product C and product D, browse blogl, blog2 and blog3, view advertisement 1, advertisement 2 , Ad 3 and Ad 4, join Circle 1 and Circle 2. M2 also purchased item B, item C and item E, browsed blogl, blog2 and bio g4, viewed advertisement 1, advertisement 2 and advertisement 5, and posted post 1 and post 2. M3 also browsed product B, product D and product F, browsed blogl, blog4 and blog5, joined circle 1, sent post 1 and post 3 «M4 also collected product B, product F and product G, browse bl〇 G2, view information 1, information 2 and capital • News 3, join circle 2, sent post 3 and post 4. M5 also purchased item B and item F, browsed bl〇g2 and blog6, browsed advertisement 4, joined circle 1 and circle 3, and browsed information 1 and information 4. These types of information are all kinds of related information associated with the product A information. The server of the step can also obtain one or more of the user information such as the age information, the academic information, the occupation information, and the family structure information of the user M1, the user M2, the user M3, the user M4, and the user M5. Step 204: According to the application platform of the user login webpage, select a related information combination that satisfies the application platform. If the visited webpage only has a combination of the information of the commodity -11 - 201030651 information and information information, the selected related information combination is A combination of product information and information. If the visited webpage has such information as product information, blog information, advertisement information, and information information, then the corresponding related information combination is selected as a combination of product information, blog information, advertisement information, and information information, according to the user login webpage. The application environment selects a combination of related information that satisfies at least two types of information such as product information, blog information, circle information, post information, product information, information information, keyword information, and advertisement information that satisfy the application environment. In the embodiment of the present invention, the visited webpage has two types of information: product information and information information, and then the corresponding related information combination is selected as a combination of commodity information and information information. Step 205: The occurrence attribute of the combination of each type of product information and information information is counted. The embodiment of the present invention describes the appearance number as an example', but is not limited thereto. In the step 203, among the users who purchase, browse or collect the product A, the number of occurrences of the product B and the information 1 of 2 people who purchase, browse or collect the product B and browse the information 1 is 2; purchase, There are 2 people who browse or collect the product F and view the information 1 again, that is, the number of occurrences of the product F and the information 1 is 2; 1 person who buys, browses or collects the product B and browses the information 2 The number of occurrences of B and Info 2 is 1; 1 person who purchases, browses, or collects item B and views information 3, that is, the number of occurrences of item B and information 3 is 1; purchase, browse, or collect goods B And there is one person who browses the information 4, that is, the number of occurrences of the product B and the information 4 is 1; the appearance of one person who purchases, browses or collects the product F and browses the information 2, that is, the appearance of the commodity F and the information 2 The number of times is 1; one person who purchases, browses, or collects goods F and views information 3, that is, the number of occurrences of 201030651 of product F and information 3 is 1; purchase, browse or collect goods F and view information 4 There are 1 person, that is, the number of occurrences of the product F and the information 4 is 1; one person who purchases, browses or collects the product G and browses the information 1 is the product G and the information 1 The number of times is 1; there is 1 person who purchases, browses or collects the product G and browses the information 2, that is, the number of occurrences of the product G and the information 2 is 1; the purchase, browsing or collection of the product G and the browsing of the information 3 There are 1 person, that is, the number of occurrences of the product G and the information 3 is 2. φ Step 2 0 6 : The server calculates the degree of association between the combination of each product information and information information and the access information, that is, the degree of association with the purchased product A, according to the result of the step 2 0 5 statistic. . Here, a collaborative filtering recommendation algorithm can be used. A recommendation algorithm that incorporates a set business rule can also be used. The combination of the product B and the information 1 and the combination of the product F and the information 1 and the number of occurrences of the commodity are the most ambiguous, and the degree of association calculated according to the number of occurrences is 2, then the association between the combination of the commodity B and the information 2 and the commodity A is sequentially calculated. Degree is 1, the degree of association between the combination of item B and information 3 and item A is Φ, the degree of association between the combination of item B and information 4 and item A is 1, and the combination of item F and information 2 is related to item A. For example, the degree of association between the combination of the item F and the information 3 and the item A is 1, and the degree of association between the combination of the item F and the information 4 and the item A is 1. In the embodiment of the present invention, the second mining may be performed according to the user information returned by the user terminal. For example, according to the user's academic qualification, the above calculated degree of association is again mined, and the degree of the user who purchases, browses, or collects the product A is counted, and it is found that the highly educated user may be interested in the product B and the information 1, and the local user. The user information returned by the terminal is also highly educated, so the weighting factor of the number of occurrences of the product-13-201030651 B and the information 1 can be set to 1, and the weighting coefficient of the number of occurrences of the commodity F and the information 1 is set to 0.6, thus finally According to the result of the user information mining, it may be that the product B and the information 1 have the highest degree of association with the currently accessed item A, the degree of association is 2, and the degree of association between the item F and the information 1 and the currently accessed item A is 1.2. In this way, the weighting coefficient is set in order for the number of occurrences of the combination of various commodity information and information information, and the degree of association between the combination of various commodity information and information information and the commodity A is obtained according to the number of occurrences of the combination of the commodity information and the information information and the corresponding weight coefficient. Step 207: Compare the degree of association calculated in step 206 with the setting ,, and when the degree of association exceeds the setting ,, acquire the associated information combination corresponding to the degree of association. In the embodiment of the present invention, the association degree 値 is 1, and in step 205, the related information combination whose degree of association is greater than 1 is a combination of the commodity B and the information 1 and a combination of the commodity F and the information 1. In the embodiment of the present invention, the related information combination of the associated information combination and the access information may be sorted, and the associated information combination corresponding to the set number of N association degrees is obtained, for example, the top two related information combinations are obtained. Step 208: The server sends the associated information combination obtained in step 207 to the client. The server converts the combination of the product B and the information 1 and the combination of the product F and the information 1 into a combination of the product B and the information 1 and the hypertext format corresponding to the combination of the product F and the information 1, and then the product B and the information 1 The combination of the product F and the information 1 and the corresponding hypertext format are sent to the local client. Alternatively, the server sends the associated information combination corresponding to the N degrees of association and the hypertext format required by the -14-201030651 to the local client. Step 209: The local user terminal displays the combination of the received product B and the information 1, the combination of the product F and the information 1, and the corresponding hypertext format to the user, or combines and associates the associated information corresponding to the N association degrees. The required hypertext format is presented to the user. According to the above method of the present invention, a device for information recommendation can be constructed, which is located in the background server. Referring to FIG. 3, the method includes: the obtaining unit 100, the calculating unit 200 and the recommending unit 300. The obtaining unit 100 is configured to obtain, after obtaining the access information of the user end, the related information associated with the access information from the memory record. The calculating unit 200 is configured to obtain an appearance attribute of the associated information combination of the at least two types of related information in the related information, and calculate the degree of association between the related information combination and the access information according to the appearance attribute of each associated information combination. . The recommendation unit 00 is configured to select the association information combination corresponding to the degree of association that satisfies the condition, and recommend the association information combination to the client. The device of the embodiment of the invention further comprises: a memory unit. The memory unit is used to store the user information and various types of information used by each user. The obtaining unit includes: a first obtaining subunit 110 and a second acquiring subunit 120°, a first obtaining subunit 110, configured to obtain, from the memory record, other users that have used the access information; and the second obtaining subunit 120 It is used to obtain various related information that the user has used from the -15-201030651 from the memory record. The calculating unit 200 further includes: a selecting subunit 210 and a statistic subunit 220 〇 selecting the subunit 210, configured to select, according to the application scenario of the user end, the associated information combination including at least two types of related information among the various types of related information. Subunit 22 0 is used to count the appearance attributes of each associated information combination. The recommendation unit 300 also includes a first selection subunit 310 and a second selection subunit 320. The first selection sub-unit 310 sorts according to the degree of association, and selects the associated information combination corresponding to the higher relevance degree. The second selection sub-unit 3 20 is configured to compare the degree of association with the setting ,, and when the degree of association exceeds the setting 値, select the associated information combination corresponding to the degree of association. The device further includes: a converting unit and a sending unit. a conversion unit, configured to convert the associated information combination into a hypertext format corresponding to the associated information combination. a sending unit, configured to send the hypertext format to the client. When the apparatus includes a user information element in the practice of the present invention, the computing unit may further include a saving subunit and a computing subunit. The user information unit is used to obtain user information of the user terminal and user information of the user terminal that has used the access information. The saving subunit is configured to save a weight coefficient set according to the user information of the user terminal and the user information of the user terminal that has visited the access information to each of the associated information combinations. The calculating subunit is configured to calculate the degree of association between the related information combination and the access information according to the appearance attribute of each associated information combination and the corresponding weight coefficient. In the embodiment of the present invention, after obtaining the access information of the user end, the server obtains various types of related information associated with the access information from the memory record, and obtains the occurrence of the associated information combination of the at least two types of related information in the related information. Attributes are respectively calculated according to the appearance attributes of each type of associated information combination, and the association degree of the related information combination is compared with the access information, and the related information combination corresponding to the conditional relevance degree is recommended to the user end, and can be performed according to the user information of the user end. Secondary mining, which can recommend the most suitable information for the user, and output different recommendation results according to any input of the user. The input can be a group of people, a circle, a product, a blog, a post, a product description or An information, etc., the output can be any combination of Φ a group of people, a circle, a commodity, a blog, a post, a product description or a message, covering all entities on the Internet, thus enabling various information flows. Interaction and personalized recommendations, improve Recommended accuracy and coverage of the recommended information, which also would increase sales and browsing traffic to your site. While the invention has been described by the embodiments of the invention in the embodiments of the invention Request to limit. -17- 201030651 [Simplified Schematic] FIG. 1 is a flowchart of a method for information recommendation according to an embodiment of the present invention; FIG. 2 is a specific flowchart of a method for information recommendation according to an embodiment of the present invention; Device structure diagram.