[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["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\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\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."]]
