推薦系統產品概述(二十五)

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"寫在前面：","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"大家好，我是強哥，一個熱愛分享的技術狂。目前已有 12 年大數據與AI相關項目經驗， 10 年推薦系統研究及實踐經驗。平時喜歡讀書、暴走和寫作。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"業餘時間專注於輸出大數據、AI等相關文章，目前已經輸出了40萬字的推薦系統系列精品文章，今年 6 月底會出版「構建企業級推薦系統：算法、工程實現與案例分析」一書。如果這些文章能夠幫助你快速入門，實現職場升職加薪，我將不勝歡喜。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"想要獲得更多免費學習資料或內推信息，一定要看到文章最後喔。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"內推信息","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如果你正在看相關的招聘信息，請加我微信：liuq4360，我這裏有很多內推資源等着你，歡迎投遞簡歷。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"免費學習資料","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如果你想獲得更多免費的學習資料，請關注同名公衆號【數據與智能】，輸入“資料”即可！","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"學習交流羣","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如果你想找到組織，和大家一起學習成長，交流經驗，也可以加入我們的學習成長羣。羣裏有老司機帶你飛，另有小哥哥、小姐姐等你來勾搭！加小姐姐微信：epsila，她會帶你入羣。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在前面的章節中我們對推薦系統的基本概念、算法原理、評估體系、工程實現等知識點進行了非常全面的介紹。在接下來的4章中我們會詳細講解推薦系統產品、設計、運營等相關的知識。這些知識雖然與推薦算法工程師的本質工作沒有直接的關係，但它們對構建一個好的、具備業務價值的工業級推薦系統是不可或缺的。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"推薦算法工程師在平常的工作中也會跟產品、運營直接接觸、溝通，因此，推薦算法工程師瞭解一些這方面的知識對於做好本職工作，幫助推薦產品更好地迭代是大有裨益的。對這些知識點的掌握與瞭解，也有利於提升推薦算法工程師的全局觀，對自身的職業發展也是大有好處的。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本章我們會講解推薦系統產品相關的知識點。具體來說，我們會從推薦產品簡介、推薦產品形態介紹、推薦產品的應用場景、設計好的推薦產品的要點等4個方面進行講述。期望讀者學習完本章後，對推薦系統產品產品形態有非常直觀的瞭解，更加重視產品設計在推薦系統價值發揮中所起的巨大作用。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"一、推薦產品簡介","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在講解之前，我們先給推薦系統產品下一個比較形式化的定義：所謂推薦系統產品，就是軟件產品(如手機中的各種APP)中基於算法或者策略爲用戶提供標的物展示的產品模塊，用戶通過與產品交互從該模塊中獲得標的物的視覺展示，最終用戶可以通過該模塊更快地“消費”標的物，該模塊在滿足用戶需求的同時提升用戶體驗、產生效果轉化。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"上面這個定義中有幾點需要說明一下：首先，推薦產品是軟件產品中的一個或者多個子模塊，每個推薦模塊就是一種推薦產品形態；其次，爲用戶展示標的物是通過算法或者策略產生的，一般來說，推薦算法是通過機器學習技術自動化地生成標的物列表，而不是人工編排的；再次，推薦產品是一個功能點，這個功能點需要通過與用戶交互才能獲得推薦列表，交互的過程是否自然流暢，對用戶體驗和效果轉化是有極大影響的；最後，推薦產品是有一定的商業目標的，目的是提升用戶體驗，形成效果轉化(對於電商推薦，轉化就是下單)。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"推薦系統涉及到兩類實體：人和標的物，推薦系統解決的就是信息匹配的問題，將標的物匹配給對該標的物有興趣的用戶，讓用戶可以看到它，進而“消費”它。匹配的準確度和及時性是非常關鍵的，這對推薦是否可以實現商業目標極爲關鍵。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"二、推薦產品形態介紹","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"所謂推薦產品形態是指產品上可以直接被用戶接觸到的各種基於算法生成的功能模塊，也就是產品上的具體功能點，用戶是可以直接看到、觸摸(觸屏交互)到的功能模塊。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"我們在《推薦算法概述》中的第1節“推薦範式”中講到了推薦系統的5種範式：完全個性化範式、羣組個性化範式、非個性化範式、標的物關聯標的物範式、笛卡爾積範式。這5種範式是根據個性化的程度(非個性化、羣組個性化、完全個性化)及實體(人和標的物)的維度來分類的，基本涵蓋了所有可能的推薦情形，在第4章中也列舉了一些產品案例。這5類推薦範式，可以從3個維度來理解。一個是用戶維度，一個是標的物維度，一個是用戶與標的物交叉維度。從用戶維度來看就是爲用戶推薦可能感興趣的標的物。從標的物維度來看，就是用戶在訪問標的物詳情頁(或者退出標的物詳情頁)時，關聯一組跟原標的物具備某種關聯的標的物列表作爲推薦。第3個維度是將用戶維度和標的物維度結合起來，不同的用戶訪問同樣的標的物詳情頁展示的標的物列表也不一樣。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"從這3個維度來描述推薦系統，更加接近用戶的直觀感受，更容易理解。下面我們分別從這3個維度來講解推薦產品形態。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"1. 基於用戶維度的推薦","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"基於用戶維度的推薦可以根據個性化的粒度分爲非個性化、羣組個性化、完全個性化。這三種粒度對應非個性化範式、羣組個性化範式、完全個性化範式。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"非個性化","attrs":{}},{"type":"text","text":"是每個用戶看到的推薦標的物都完全一樣，傳統門戶網站的編輯對內容的編排就是非個性化的方式，每個用戶看到的標的物都是一樣的。對於各類網站或者APP的排行榜的推薦形態就是非個性化的。某雲音樂的排行榜推薦，根據各個維度計算各類榜單。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"羣組個性化","attrs":{}},{"type":"text","text":"就是將相同特徵的用戶聚合成一組，同一組用戶在某些特徵上具備相似性，我們爲這一組用戶推薦完全一樣的標的物。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"精細化運營一般會採用該方式，通過用戶畫像系統圈定一批人(具備相同標籤的一組用戶)，並對這批人做統一的運營。比如視頻行業的會員精細化運營，當會員快到期時，可以藉助精準運營留住用戶，具體做法是：將快到期的會員用戶圈出來，針對這批用戶做會員打折活動，促進用戶產生新的購買，從而留住會員用戶。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"某貓電視劇頻道”戰爭風雲“tab的基於羣組的個性化重排序。我們將用戶根據興趣分組(聚類)，同一組內的用戶看到的內容是一樣的順序，不同組的用戶的排序是不一樣的。但是不管哪個用戶其實看到的內容集合(戰爭風雲tab的全部內容)是相同的，只不過根據用戶的興趣做了排序，把用戶更喜歡的內容排在了前面。重排序推薦就是限定標的物範圍下的個性化排序，有點類似命題作文。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"對於某貓這類購物網站來說，對未登錄用戶或冷啓動用戶，可以採用基於人羣屬性來做推薦。通過將用戶按照性別、年齡段、收貨城市等粗粒度的屬性劃分爲若干人羣，然後基於每類人羣的行爲數據挑選出該人羣點擊率最高的TopK個商品作爲該人羣感興趣的商品推薦給他們。該方法也是一種羣組個性化冷啓動策略。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"完全個性化","attrs":{}},{"type":"text","text":"就是爲每個用戶推薦的內容都不一樣，是根據用戶的行爲及興趣來爲用戶做推薦，是最主流的一種推薦形式。大多數時候我們所說的推薦就是指這種形式的推薦。某寶首頁的猜你喜歡推薦，這個推薦就是完全個性化的，每個人推薦的都不一樣。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"完全個性化也可以基於用戶的好友關係來做推薦。V信上線的好物推薦，是基於社交關係的個性化推薦，將你的好友買過的商品推薦給你。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"從另外一個角度看，完全個性化推薦可以分爲只基於用戶個人行爲的推薦和基於羣體行爲的推薦。基於個人行爲的推薦，在構建推薦算法時只依賴個人的行爲，不需要其他用戶的行爲，常見的基於內容推薦就是這類推薦算法。基於羣體行爲的推薦，除了利用自己的行爲外，還依賴其他用戶的行爲來構建算法模型，這類推薦算法可以認爲是全體用戶行爲的“協同進化“，像協同過濾、分解機、深度學習等推薦算法都是這類推薦形式。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin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✖️ 標的物數，對於互聯網公司，這個數量是巨大的)，我們必須在用戶請求的過程中快速地爲用戶計算個性化的推薦列表，這對整個推薦系統的架構有更高的要求，所以在實際場景中用得比較少，我們在《推薦系統提供web服務的2種方式》中也講到了這種實時裝配型的推薦服務形態。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"上面我們從3個維度講解了推薦產品形態。在實際業務中最主要的產品形態是關聯推薦、個性化推薦。關聯推薦就是上面提到的基於標的物維度的推薦，關聯推薦之所以重要，是因爲該推薦產品形態是一種用戶觸點多的產品形態，用戶在產品上的任何有效行爲最終都會進入詳情頁，該產品形態跟用戶的接觸面廣，流量大。在電視貓中，關聯推薦在所有推薦產品中所產生的播放佔比接近50%，佔了推薦系統的半壁江山。個性化推薦就是我們上面提到的完全個性化推薦，爲每個用戶都提供不一樣的推薦，這類推薦一般可以部署到產品的首頁，產品首頁是流量最大的地方，是用戶的必經之地。如果推薦做得好，可以產生極大的商業價值。現在淘寶、京東、拼多多首頁都已經個性化了，並且都做到了實時個性化推薦。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"另外一些比較常見的、耳熟能詳的推薦產品形態有：排行榜推薦、信息流推薦等。排行榜推薦就是上面提到的非個性化推薦。信息流推薦是完全個性化推薦，只不過是採用實時信息流的方式與用戶進行交互的。隨着頭條、抖音、快手的流行，信息流推薦越來越受到業界的重視，在產品中具備極大的商業價值。信息流推薦比較適合提供“快消”類標的物的產品，用戶可以在碎片化時間中獲得更好的使用體驗。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"目前業界有非常多的很好的推薦產品形態值得讀者瞭解和學習，大家耳熟能詳的就是某條這種下拉的信息流推薦。是陌生人社交APP某探上的左右滑動的推薦產品形態，這種產品形態是一種用戶體驗很好的嘗試，用戶操作簡單、直接，你喜歡這個女的就右滑，不喜歡就左滑。某寶首頁當用戶查看某克鞋詳情頁退出後在某克鞋縮略圖下面展示4個相關鞋子的推薦，下圖最右邊是網易新聞當用戶播放一個視頻幾秒鐘後，在該視頻下面給用戶提供一組相似推薦，當該視頻播放完會直接連播後面的相似視頻。這兩類相似推薦在前面也提到了，它們就是非常好的標的物關聯推薦產品形態，對用戶沒有干擾，用戶也不需要進行復雜的操作就可以獲得推薦，推薦非常直觀、自然，毫無違和感。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"上面只列舉了幾個設計得比較好的工業級推薦系統的產品形態，這些好的產品形態都值得讀者借鑑。更多好的推薦產品形態等待讀者結合自己公司業務情況去探索、嘗試。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"三、推薦產品的應用場景","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"我們在第1章中就講到，推薦系統是隨着信息技術的發展而逐步產生而發展起來的，只要產品提供的標的物數量足夠多，用戶無法手動從所有標的物中篩選出自己感興趣的，就具備了做個性化推薦的條件。因此適合做個性化推薦的軟件產品是非常多的，下面這些產品都適合做個性化推薦。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l 電商網站：某寶，某東，某遜等","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l 視頻：某x，某酷，某音某手,某貓等","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l 音樂：某雲音樂，某狗音樂等","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l 資訊類：某條，某快報等","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l 生活服務類：某團，某程，某脈等","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l 交友類：某陌，某愛網等","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"不同行業的產品雖說都可以提供第2節中的推薦產品形態，但是在具體落地時是不一樣的，需要考慮到具體的產品功能和使用場景進行調整，即所謂的場景化推薦：基於時間、地理位置、上下文等提供差異化的推薦。下面對幾類有代表性的場景加以簡單說明。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l ","attrs":{}},{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"基於時間的場景","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"某T(家庭互聯網)行業，由於家庭中有多個成員，每個成員活動的時間不一樣(老年人平時都在家，年輕的父母工作日要上班，而小孩白天要上學)，每個人的興趣需求也不一樣，因此給他們提供的推薦需要在不同時段具備差異性，滿足家庭中每個個體的需求。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"l ","attrs":{}},{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"基於地理位置的場景","attrs":{}}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