依據內容篩選
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
依據內容篩選功能會使用項目功能推薦其他項目
根據使用者先前的操作或
提供意見回饋。
為了示範依內容篩選的功能，我們
。下圖顯示特徵矩陣
每一列都代表一個應用程式，每欄代表一個特徵。功能與特色
可能包含類別 (例如教育、休閒、健康)、
等等為了簡單說明，假設這個特徵矩陣是二進位：
非零的值表示應用程式有該功能。
也可以在同一個功能空間代表使用者。一些使用者相關
功能可以明確提供舉例來說，使用者選取
「娛樂應用程式」。其他功能並不簡單
根據使用者先前安裝的應用程式
進行追蹤舉例來說，使用者
安裝了另一個由 Science R Us 發布的應用程式。
模型應推薦與這位使用者相關的商品。如要這麼做，您必須
首先選擇相似度指標 (例如內點產品)接著，您必須
也就是讓系統根據此相似度為每個候選項目評分
指標。請注意，這些建議僅適用於這位使用者，因為
不使用任何有關其他使用者的資訊。
使用內積計算相似度量
請考慮使用者嵌入 \(x\) 和應用程式
嵌入 \(y\) 都是二進位向量開始時間
\(\langle x, y \rangle = \sum_{i = 1}^d x_i y_i\)、
同時出現在兩者中， \(x\) 而且 \(y\) 貢獻 1 到 1
總和也就是說， \(\langle x, y \rangle\) 是數字
兩個向量內作用的特徵資訊高
藉此得出更共同的特徵，因此相似度也較高。
你自己試試看！
針對上述應用程式問題中的每個應用程式計算內積。
然後使用該資訊回答以下問題：
請問該推薦哪個應用程式？
Science R Us 打造的教育應用程式
答對了！這個商品的中點產品最高
2。我們的使用者很喜歡科學和教育類應用程式，
由 Healthcare 建立的健康應用程式。
這個應用程式的分數為 1。因為這不是我們推薦的
但絕對不是最好的證明
由 TimeWastr 開發的休閒應用程式。
這個應用程式實際上的點積是最低的 0。我們的
使用者對遊戲等休閒應用程式不感興趣。
除非另有註明，否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權，程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間：2024-07-26 (世界標準時間)。
[null,null,["上次更新時間：2024-07-26 (世界標準時間)。"],[[["\u003cp\u003eContent-based filtering suggests items similar to a user's preferences by analyzing item features and user interactions.\u003c/p\u003e\n"],["\u003cp\u003eUser and item features are represented in a feature matrix, where common features indicate higher similarity.\u003c/p\u003e\n"],["\u003cp\u003eDot product is used as a similarity metric, with higher values indicating stronger relevance between user and item.\u003c/p\u003e\n"],["\u003cp\u003eRecommendations are tailored to individual users based on their specific features and interactions, without using data from other users.\u003c/p\u003e\n"],["\u003cp\u003eThe system identifies the best recommendations by calculating dot products and selecting items with the highest scores.\u003c/p\u003e\n"]]],[],null,["# Content-based filtering uses item features to recommend other items\nsimilar to what the user likes, based on their previous actions or explicit\nfeedback.\n\nTo demonstrate content-based filtering, let's hand-engineer some features\nfor the Google Play store. The following figure shows a feature matrix where\neach row represents an app and each column represents a feature. Features\ncould include categories (such as Education, Casual, Health), the publisher\nof the app, and many others. To simplify, assume this feature matrix is binary:\na non-zero value means the app has that feature.\n\nYou also represent the user in the same feature space. Some of the user-related\nfeatures could be explicitly provided by the user. For example, a user selects\n\"Entertainment apps\" in their profile. Other features can be implicit,\nbased on the apps they have previously installed. For example, the user\ninstalled another app published by Science R Us.\n\nThe model should recommend items relevant to this user. To do so, you must\nfirst pick a similarity metric (for example, dot product). Then, you must\nset up the system to score each candidate item according to this similarity\nmetric. Note that the recommendations are specific to this user, as the model\ndid not use any information about other users.\n\nUsing dot product as a similarity measure\n-----------------------------------------\n\nConsider the case where the user embedding \\\\(x\\\\) and the app\nembedding \\\\(y\\\\) are both binary vectors. Since\n\\\\(\\\\langle x, y \\\\rangle = \\\\sum_{i = 1}\\^d x_i y_i\\\\), a\nfeature appearing in both \\\\(x\\\\) and \\\\(y\\\\) contributes a 1 to\nthe sum. In other words, \\\\(\\\\langle x, y \\\\rangle\\\\) is the number\nof features that are active in both vectors simultaneously. A high\ndot product then indicates more common features, thus a higher similarity.\n\nTry it yourself!\n----------------\n\nCalculate the dot product for each app in the preceding app problem.\nThen use that information to answer the question below: \nWhich app should we recommend? \nThe educational app created by Science R Us. \nYou are correct! This item has the highest dot product at 2. Our user really likes science and educational apps. \nThe health app created by Healthcare. \nThis app scores a 1. It isn't the worst recommendation our system could make, but it certainly isn't the best. \nThe casual app created by TimeWastr. \nThis app actually has the lowest dot product at 0. Our user isn't interested in casual apps like games."]]
