{"id":25736,"date":"2024-03-16T05:29:28","date_gmt":"2024-03-16T05:29:28","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/"},"modified":"2024-03-22T06:53:35","modified_gmt":"2024-03-22T06:53:35","slug":"implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/","title":{"rendered":"Implementing a collaborative filtering algorithm based on matrix factorization."},"content":{"rendered":"<p>Matrix Factorization is a commonly used Collaborative Filtering (CF) algorithm, often utilized in recommendation systems. Below is an implementation example of a CF algorithm based on matrix factorization.<\/p>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\r\n\r\n<span class=\"hljs-keyword\">class<\/span> <span class=\"hljs-title class_\">MatrixFactorizationCF<\/span>:\r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">__init__<\/span>(<span class=\"hljs-params\">self, num_users, num_items, num_factors=<span class=\"hljs-number\">10<\/span>, learning_rate=<span class=\"hljs-number\">0.01<\/span>, reg_param=<span class=\"hljs-number\">0.01<\/span>, num_iterations=<span class=\"hljs-number\">100<\/span><\/span>):\r\n        self.num_users = num_users\r\n        self.num_items = num_items\r\n        self.num_factors = num_factors\r\n        self.learning_rate = learning_rate\r\n        self.reg_param = reg_param\r\n        self.num_iterations = num_iterations\r\n        self.user_factors = <span class=\"hljs-literal\">None<\/span>\r\n        self.item_factors = <span class=\"hljs-literal\">None<\/span>\r\n    \r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">fit<\/span>(<span class=\"hljs-params\">self, train_data<\/span>):\r\n        <span class=\"hljs-comment\"># \u521d\u59cb\u5316\u7528\u6237\u548c\u7269\u54c1\u7684\u9690\u56e0\u5b50\u77e9\u9635<\/span>\r\n        self.user_factors = np.random.normal(scale=<span class=\"hljs-number\">1.<\/span>\/self.num_factors, size=(self.num_users, self.num_factors))\r\n        self.item_factors = np.random.normal(scale=<span class=\"hljs-number\">1.<\/span>\/self.num_factors, size=(self.num_items, self.num_factors))\r\n        \r\n        <span class=\"hljs-keyword\">for<\/span> iteration <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(self.num_iterations):\r\n            <span class=\"hljs-keyword\">for<\/span> user_id, item_id, rating <span class=\"hljs-keyword\">in<\/span> train_data:\r\n                error = rating - self.predict(user_id, item_id)\r\n                \r\n                <span class=\"hljs-comment\"># \u66f4\u65b0\u7528\u6237\u548c\u7269\u54c1\u7684\u9690\u56e0\u5b50\u77e9\u9635<\/span>\r\n                self.user_factors[user_id] += self.learning_rate * (error * self.item_factors[item_id] - self.reg_param * self.user_factors[user_id])\r\n                self.item_factors[item_id] += self.learning_rate * (error * self.user_factors[user_id] - self.reg_param * self.item_factors[item_id])\r\n    \r\n    <span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title function_\">predict<\/span>(<span class=\"hljs-params\">self, user_id, item_id<\/span>):\r\n        <span class=\"hljs-keyword\">return<\/span> np.dot(self.user_factors[user_id], self.item_factors[item_id])\r\n<\/code><\/pre>\n<p>Example of use:<\/p>\n<pre class=\"post-pre\"><code><span class=\"hljs-comment\"># \u521b\u5efa\u4e00\u4e2a\u77e9\u9635\u5206\u89e3\u7684CF\u6a21\u578b<\/span>\r\ncf_model = MatrixFactorizationCF(num_users=<span class=\"hljs-number\">100<\/span>, num_items=<span class=\"hljs-number\">50<\/span>, num_factors=<span class=\"hljs-number\">10<\/span>, learning_rate=<span class=\"hljs-number\">0.01<\/span>, reg_param=<span class=\"hljs-number\">0.01<\/span>, num_iterations=<span class=\"hljs-number\">100<\/span>)\r\n\r\n<span class=\"hljs-comment\"># \u4f7f\u7528\u8bad\u7ec3\u6570\u636e\u8bad\u7ec3\u6a21\u578b<\/span>\r\ntrain_data = [(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">5<\/span>), (<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">3<\/span>), (<span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">4<\/span>), ...]\r\ncf_model.fit(train_data)\r\n\r\n<span class=\"hljs-comment\"># \u9884\u6d4b\u7528\u62370\u5bf9\u7269\u54c11\u7684\u8bc4\u5206<\/span>\r\nuser_id = <span class=\"hljs-number\">0<\/span>\r\nitem_id = <span class=\"hljs-number\">1<\/span>\r\npredicted_rating = cf_model.predict(user_id, item_id)\r\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Predicted rating for user\"<\/span>, user_id, <span class=\"hljs-string\">\"and item\"<\/span>, item_id, <span class=\"hljs-string\">\":\"<\/span>, predicted_rating)\r\n<\/code><\/pre>\n<p>The above example illustrates how to use matrix factorization-based CF algorithm to predict ratings of users on items. In the fit method, the hidden factor matrices of users and items are iteratively optimized to approximate the actual rating data. Then, the predict method is used to predict ratings of users on items.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Matrix Factorization is a commonly used Collaborative Filtering (CF) algorithm, often utilized in recommendation systems. Below is an implementation example of a CF algorithm based on matrix factorization. import numpy as np class MatrixFactorizationCF: def __init__(self, num_users, num_items, num_factors=10, learning_rate=0.01, reg_param=0.01, num_iterations=100): self.num_users = num_users self.num_items = num_items self.num_factors = num_factors self.learning_rate = learning_rate self.reg_param [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-25736","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Implementing a collaborative filtering algorithm based on matrix factorization. - Blog - Silicon Cloud<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Implementing a collaborative filtering algorithm based on matrix factorization.\" \/>\n<meta property=\"og:description\" content=\"Matrix Factorization is a commonly used Collaborative Filtering (CF) algorithm, often utilized in recommendation systems. Below is an implementation example of a CF algorithm based on matrix factorization. import numpy as np class MatrixFactorizationCF: def __init__(self, num_users, num_items, num_factors=10, learning_rate=0.01, reg_param=0.01, num_iterations=100): self.num_users = num_users self.num_items = num_items self.num_factors = num_factors self.learning_rate = learning_rate self.reg_param [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - Silicon Cloud\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/SiliCloudGlobal\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-03-16T05:29:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-22T06:53:35+00:00\" \/>\n<meta name=\"author\" content=\"Ava Mitchell\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@SiliCloudGlobal\" \/>\n<meta name=\"twitter:site\" content=\"@SiliCloudGlobal\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ava Mitchell\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/\"},\"author\":{\"name\":\"Ava Mitchell\",\"@id\":\"https:\/\/www.silicloud.com\/blog\/#\/schema\/person\/a3e2658c2cb9fb2be95ae0a8861f4a64\"},\"headline\":\"Implementing a collaborative filtering algorithm based on matrix factorization.\",\"datePublished\":\"2024-03-16T05:29:28+00:00\",\"dateModified\":\"2024-03-22T06:53:35+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/\"},\"wordCount\":92,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.silicloud.com\/blog\/#organization\"},\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/\",\"url\":\"https:\/\/www.silicloud.com\/blog\/implementing-a-collaborative-filtering-algorithm-based-on-matrix-factorization\/\",\"name\":\"Implementing a collaborative filtering algorithm based on matrix factorization. - 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