{"id":476320,"date":"2023-08-09T07:28:31","date_gmt":"2023-08-09T07:28:31","guid":{"rendered":""},"modified":"2023-09-05T11:12:27","modified_gmt":"2023-09-05T11:12:27","slug":"collaborative-filtering","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/ar\/wiki\/collaborative-filtering\/","title":{"rendered":"\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629"},"content":{"rendered":"<p>\u062a\u0639\u062f \u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629 (CF) \u0637\u0631\u064a\u0642\u0629 \u062e\u0648\u0627\u0631\u0632\u0645\u064a\u0629 \u0642\u0648\u064a\u0629 \u064a\u062a\u0645 \u062a\u0637\u0628\u064a\u0642\u0647\u0627 \u0628\u0634\u0643\u0644 \u0645\u062a\u0643\u0631\u0631 \u0641\u064a \u0645\u062c\u0627\u0644 \u0623\u0646\u0638\u0645\u0629 \u0627\u0644\u062a\u0648\u0635\u064a\u0629. \u0641\u0631\u0636\u064a\u062a\u0647\u0627 \u0627\u0644\u0623\u0633\u0627\u0633\u064a\u0629 \u0647\u064a 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\u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629 \u0648\u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0645\u0633\u062a\u0646\u062f\u0629 \u0625\u0644\u0649 \u0627\u0644\u0645\u062d\u062a\u0648\u0649\u060c \u0628\u0647\u062f\u0641 \u062a\u062c\u0646\u0628 \u0628\u0639\u0636 \u0627\u0644\u0642\u064a\u0648\u062f.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0648\u062c\u0647\u0627\u062a \u0646\u0638\u0631 \u0645\u0633\u062a\u0642\u0628\u0644\u064a\u0629 \u0628\u0634\u0623\u0646 \u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629<\/h2>\n<p>\u0645\u0639 \u0638\u0647\u0648\u0631 \u062a\u0642\u0646\u064a\u0627\u062a \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0648\u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0627\u0644\u0623\u0643\u062b\u0631 \u062a\u0637\u0648\u0631\u064b\u0627\u060c \u062a\u062a\u0637\u0648\u0631 \u0623\u0633\u0627\u0644\u064a\u0628 CF. \u062a\u064f\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0622\u0646 \u062a\u0642\u0646\u064a\u0627\u062a \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0639\u0645\u064a\u0642 \u0644\u062a\u0637\u0648\u064a\u0631 \u0646\u0645\u0627\u0630\u062c \u0645\u0639\u0642\u062f\u0629 \u0644\u0644\u062a\u0644\u064a\u0641 \u0627\u0644\u0643\u064a\u0633\u064a\u060c \u0648\u062a\u0642\u062f\u064a\u0645 \u062a\u0648\u0635\u064a\u0627\u062a \u0623\u0643\u062b\u0631 \u062f\u0642\u0629. \u0639\u0644\u0627\u0648\u0629 \u0639\u0644\u0649 \u0630\u0644\u0643\u060c \u0641\u0625\u0646 \u0627\u0644\u0628\u062d\u062b \u0641\u064a \u0645\u0639\u0627\u0644\u062c\u0629 \u062a\u062d\u062f\u064a\u0627\u062a \u0634\u062d \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u0645\u0634\u0643\u0644\u0629 \u0627\u0644\u0628\u062f\u0627\u064a\u0629 \u0627\u0644\u0628\u0627\u0631\u062f\u0629 \u0645\u0633\u062a\u0645\u0631\u060c \u0645\u0645\u0627 \u064a\u0639\u062f \u0628\u0637\u0631\u0642 \u0623\u0643\u062b\u0631 \u0643\u0641\u0627\u0621\u0629 \u0648\u0641\u0639\u0627\u0644\u064a\u0629 \u0644\u0644\u062a\u0644\u064a\u0641 \u0627\u0644\u0643\u064a\u0633\u064a \u0641\u064a \u0627\u0644\u0645\u0633\u062a\u0642\u0628\u0644.<\/p>\n<h2>\u0627\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u0648\u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629<\/h2>\n<p>\u064a\u0645\u0643\u0646 \u0644\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629\u060c \u0645\u062b\u0644 \u062a\u0644\u0643 \u0627\u0644\u062a\u064a \u064a\u0648\u0641\u0631\u0647\u0627 OneProxy\u060c \u0623\u0646 \u062a\u0633\u0627\u0639\u062f \u0628\u0634\u0643\u0644 \u063a\u064a\u0631 \u0645\u0628\u0627\u0634\u0631 \u0641\u064a \u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629. \u0623\u0646\u0647\u0627 \u062a\u0648\u0641\u0631 \u0639\u062f\u0645 \u0627\u0644\u0643\u0634\u0641 \u0639\u0646 \u0647\u0648\u064a\u062a\u0647 \u0648\u0627\u0644\u0623\u0645\u0627\u0646\u060c \u0645\u0645\u0627 \u064a\u0633\u0645\u062d \u0644\u0644\u0645\u0633\u062a\u062e\u062f\u0645\u064a\u0646 \u0628\u0627\u0644\u062a\u0635\u0641\u062d \u0628\u062e\u0635\u0648\u0635\u064a\u0629. \u0648\u0647\u0630\u0627 \u064a\u0634\u062c\u0639 \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645\u064a\u0646 \u0639\u0644\u0649 \u0627\u0644\u062a\u0641\u0627\u0639\u0644 \u0628\u062d\u0631\u064a\u0629 \u0645\u0639 \u0627\u0644\u0639\u0646\u0627\u0635\u0631 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0639\u0644\u0649 \u0627\u0644\u0625\u0646\u062a\u0631\u0646\u062a \u062f\u0648\u0646 \u062e\u0648\u0641 \u0645\u0646 \u0627\u0644\u0645\u0633\u0627\u0633 \u0628\u062e\u0635\u0648\u0635\u064a\u062a\u0647\u0645. \u062a\u0639\u062a\u0628\u0631 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0646\u0627\u062a\u062c\u0629 \u0636\u0631\u0648\u0631\u064a\u0629 \u0644\u0640 CF\u060c \u062d\u064a\u062b \u0623\u0646\u0647\u0627 \u062a\u0639\u062a\u0645\u062f \u0628\u0634\u0643\u0644 \u0643\u0628\u064a\u0631 \u0639\u0644\u0649 \u062a\u0641\u0627\u0639\u0644\u0627\u062a \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645 \u0645\u0639 \u0627\u0644\u0639\u0646\u0635\u0631 \u0644\u062a\u0642\u062f\u064a\u0645 \u0627\u0644\u062a\u0648\u0635\u064a\u0627\u062a.<\/p>\n<h2>\u0631\u0648\u0627\u0628\u0637 \u0630\u0627\u062a \u0639\u0644\u0627\u0642\u0629<\/h2>\n<ol>\n<li><a href=\"http:\/\/grouplens.org\/\" target=\"_new\" rel=\"noopener nofollow\">\u0623\u0628\u062d\u0627\u062b GroupLens<\/a><\/li>\n<li><a href=\"https:\/\/research.netflix.com\/\" target=\"_new\" rel=\"noopener nofollow\">\u0623\u0628\u062d\u0627\u062b \u0646\u064a\u062a\u0641\u0644\u064a\u0643\u0633<\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.science\/tag\/recommendation\" target=\"_new\" rel=\"noopener nofollow\">\u0623\u0628\u062d\u0627\u062b \u0627\u0644\u0623\u0645\u0627\u0632\u0648\u0646<\/a><\/li>\n<li><a href=\"https:\/\/dl.acm.org\/\" target=\"_new\" rel=\"noopener nofollow\">\u0645\u0643\u062a\u0628\u0629 ACM \u0627\u0644\u0631\u0642\u0645\u064a\u0629<\/a> \u0644\u0644\u0628\u062d\u062b \u0627\u0644\u0623\u0643\u0627\u062f\u064a\u0645\u064a \u062d\u0648\u0644 \u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629<\/li>\n<li><a href=\"https:\/\/scholar.google.com\/\" target=\"_new\" rel=\"noopener nofollow\">\u0645\u0646\u062d\u0629 \u062c\u0648\u062c\u0644<\/a> \u0644\u0644\u0623\u0648\u0631\u0627\u0642 \u0627\u0644\u0623\u0643\u0627\u062f\u064a\u0645\u064a\u0629 \u062d\u0648\u0644 \u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u062a\u0639\u0627\u0648\u0646\u064a\u0629<\/li>\n<\/ol>","protected":false},"featured_media":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476320","wiki","type-wiki","status-publish","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Collaborative Filtering: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Collaborative Filtering?","answer":"<p>Collaborative Filtering (CF) is an algorithmic method used within recommendation systems to predict a specific user's interests based on the preferences collected from numerous users.<\/p>"},{"question":"What is the history of Collaborative Filtering?","answer":"<p>The term Collaborative Filtering was first introduced in the GroupLens project by the University of Minnesota in 1994, which was designed for Usenet news. However, the concept was first mentioned in 1992 by David Goldberg and others from Xerox PARC, who developed Tapestry, an early email system that allowed users to filter messages based on tags.<\/p>"},{"question":"How does Collaborative Filtering work?","answer":"<p>Collaborative Filtering works by creating a user-item matrix, which is filled with the preferences (such as ratings) given by users to items. It then calculates the similarity between users or items, predicts the ratings of the items not yet rated by a user, and recommends the top-N items with the highest predicted ratings.<\/p>"},{"question":"What are the key features of Collaborative Filtering?","answer":"<p>The key features of Collaborative Filtering include personalization, adaptability, and scalability. However, it does have challenges such as the cold start problem, which is when there is insufficient data to make accurate recommendations for new users or items.<\/p>"},{"question":"What types of Collaborative Filtering exist?","answer":"<p>There are three main types of Collaborative Filtering: Memory-based CF that uses the memory of previous users' interactions to compute user or item similarity, Model-based CF that learns a model to predict user preferences, and Hybrid CF that combines the Memory-based and Model-based methods to overcome certain limitations.<\/p>"},{"question":"How is Collaborative Filtering used and what are the associated problems?","answer":"<p>Collaborative Filtering is used in various domains such as movies, music, news, books, research articles, search queries, social tags, and general products. The associated challenges include the cold start problem, sparsity, and scalability. However, solutions exist, such as hybrid models, dimensionality reduction techniques, and the use of more scalable algorithms.<\/p>"},{"question":"How does Collaborative Filtering compare to similar techniques?","answer":"<p>Collaborative Filtering is based on the assumption that users will like things similar to what they liked in the past and things liked by people with similar tastes. This contrasts with Content-Based Filtering, which recommends items by comparing the content of the items and a user profile. Hybrid Methods combine Collaborative Filtering and Content-Based Filtering to avoid certain limitations.<\/p>"},{"question":"What are the future perspectives on Collaborative Filtering?","answer":"<p>The future of Collaborative Filtering includes the advent of more sophisticated machine learning and artificial intelligence technologies. Deep learning techniques are being used to develop complex models for CF, providing more accurate recommendations. Ongoing research aims to address challenges of data sparsity and the cold start problem.<\/p>"},{"question":"How are proxy servers associated with Collaborative Filtering?","answer":"<p>Proxy servers can indirectly aid in Collaborative Filtering by providing anonymity and security, which allows users to browse with privacy. This encourages users to freely interact with items on the internet without fearing to compromise their privacy, leading to more user-item interaction data that CF relies on for making recommendations.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki\/476320","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki\/476320\/revisions"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/media?parent=476320"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}