{"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\/tr\/wiki\/collaborative-filtering\/","title":{"rendered":"\u0130\u015fbirlik\u00e7i filtreleme"},"content":{"rendered":"<p>\u0130\u015fbirlik\u00e7i Filtreleme (CF), \u00f6neri sistemleri alan\u0131nda s\u0131kl\u0131kla uygulanan g\u00fc\u00e7l\u00fc bir algoritmik y\u00f6ntemdir. Temel dayana\u011f\u0131, bir\u00e7ok kullan\u0131c\u0131n\u0131n tercihlerini toplayarak belirli bir kullan\u0131c\u0131n\u0131n \u00e7\u0131karlar\u0131n\u0131 tahmin etmektir. CF&#039;nin temelini olu\u015fturan varsay\u0131m, e\u011fer iki kullan\u0131c\u0131 bir konu \u00fczerinde anla\u015f\u0131rsa, muhtemelen ba\u015fkalar\u0131 \u00fczerinde de anla\u015fmaya varacaklar\u0131d\u0131r.<\/p>\n<h2>\u0130\u015fbirlik\u00e7i Filtrelemenin Do\u011fu\u015fu ve Evrimi<\/h2>\n<p>\u0130\u015fbirli\u011fine Dayal\u0131 Filtreleme&#039;den ilk kez 1992 y\u0131l\u0131nda David Goldberg ve Xerox PARC&#039;tan di\u011ferleri taraf\u0131ndan eski bir e-posta sistemi olan Tapestry&#039;nin geli\u015ftirilmesi s\u0131ras\u0131nda bahsedildi. Goblen, insan zekas\u0131n\u0131 kullanacak ve insanlar\u0131n gelen mesajlara daha sonra mesajlar\u0131 filtrelemek i\u00e7in kullan\u0131labilecek a\u00e7\u0131klamalar veya &quot;etiketler&quot; eklemesine olanak tan\u0131yacak \u015fekilde tasarland\u0131.<\/p>\n<p>1994 y\u0131l\u0131nda Minnesota \u00dcniversitesi&#039;nin GroupLens projesi, otomatik bir CF yakla\u015f\u0131m\u0131 \u00f6nererek &quot;i\u015fbirlik\u00e7i filtreleme&quot; terimini tan\u0131tt\u0131. Bu proje, kullan\u0131c\u0131lar\u0131n payla\u015f\u0131mda bulunabilece\u011fi ve tercihlerine g\u00f6re filtreleyebilecekleri bir haber gruplar\u0131 a\u011f\u0131 olan Usenet haberleri i\u00e7in CF&#039;yi kulland\u0131.<\/p>\n<h2>\u0130\u015fbirli\u011fine Dayal\u0131 Filtrelemenin Geli\u015ftirilmesi<\/h2>\n<p>\u0130\u015fbirlik\u00e7i filtreleme esas olarak, kullan\u0131c\u0131lar taraf\u0131ndan \u00f6\u011felere verilen tercihleri (derecelendirmeler gibi) i\u00e7eren bir kullan\u0131c\u0131 \u00f6\u011fesi matrisi olu\u015fturarak \u00e7al\u0131\u015f\u0131r. \u00d6rne\u011fin bir film \u00f6neri sistemi ba\u011flam\u0131nda bu matris, kullan\u0131c\u0131lar\u0131n farkl\u0131 filmlere verdi\u011fi derecelendirmeleri i\u00e7erecektir.<\/p>\n<p>CF iki temel paradigmaya dayanmaktad\u0131r: Bellek tabanl\u0131 CF ve Model tabanl\u0131 CF.<\/p>\n<ul>\n<li>\n<p>Bellek Tabanl\u0131 CF: Mahalle tabanl\u0131 CF olarak da bilinen bu paradigma, kullan\u0131c\u0131lar veya \u00f6\u011feler aras\u0131ndaki benzerli\u011fe dayal\u0131 tahminler yapar. Kullan\u0131c\u0131-Kullan\u0131c\u0131 CF&#039;si (tahmin edilen kullan\u0131c\u0131ya benzer kullan\u0131c\u0131lar\u0131 tan\u0131mlar) ve \u00d6\u011fe-\u00d6\u011fe CF&#039;si (kullan\u0131c\u0131n\u0131n derecelendirdiklerine benzer \u00f6\u011feleri tan\u0131mlar) olarak alt b\u00f6l\u00fcmlere ayr\u0131lm\u0131\u015ft\u0131r.<\/p>\n<\/li>\n<li>\n<p>Model Tabanl\u0131 CF: Bu yakla\u015f\u0131m, tercihlerini \u00f6\u011frenmek i\u00e7in bir kullan\u0131c\u0131 modeli geli\u015ftirmeyi i\u00e7erir. \u0130lgili teknikler k\u00fcmeleme, matris \u00e7arpanlar\u0131na ay\u0131rma, derin \u00f6\u011frenme vb.&#039;dir.<\/p>\n<\/li>\n<\/ul>\n<h2>\u0130\u015fbirli\u011fine Dayal\u0131 Filtrelemenin Arkas\u0131ndaki Mekanizma<\/h2>\n<p>\u0130\u015fbirli\u011fine Dayal\u0131 Filtreleme s\u00fcre\u00e7leri \u00f6z\u00fcnde iki ad\u0131mdan olu\u015fur: benzer zevklere sahip kullan\u0131c\u0131lar\u0131 bulmak ve bu benzer kullan\u0131c\u0131lar\u0131n tercihlerine g\u00f6re \u00f6\u011feler \u00f6nermek. \u0130\u015fte i\u015fleyi\u015finin genel bir tasla\u011f\u0131:<\/p>\n<ol>\n<li>Kullan\u0131c\u0131lar veya \u00f6\u011feler aras\u0131ndaki benzerli\u011fi hesaplay\u0131n.<\/li>\n<li>Hen\u00fcz kullan\u0131c\u0131 taraf\u0131ndan derecelendirilmemi\u015f \u00f6\u011felerin derecelendirmelerini tahmin edin.<\/li>\n<li>Tahmin edilen en y\u00fcksek derecelendirmeye sahip ilk N \u00f6\u011feyi \u00f6nerin.<\/li>\n<\/ol>\n<p>Kullan\u0131c\u0131lar veya \u00f6\u011feler aras\u0131ndaki benzerlik genellikle kosin\u00fcs benzerli\u011fi veya Pearson korelasyonu kullan\u0131larak hesaplan\u0131r.<\/p>\n<h2>\u0130\u015fbirlik\u00e7i Filtrelemenin Temel \u00d6zellikleri<\/h2>\n<ol>\n<li><strong>Ki\u015fiselle\u015ftirme:<\/strong> CF, \u00f6neride bulunurken bireysel kullan\u0131c\u0131n\u0131n davran\u0131\u015f\u0131n\u0131 dikkate ald\u0131\u011f\u0131 i\u00e7in ki\u015fiye \u00f6zel \u00f6neriler sunar.<\/li>\n<li><strong>Uyarlanabilirlik:<\/strong> Kullan\u0131c\u0131n\u0131n de\u011fi\u015fen ilgi alanlar\u0131na uyum sa\u011flayabilir.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik:<\/strong> CF algoritmalar\u0131 b\u00fcy\u00fck miktarda veriyle ba\u015f etme kapasitesine sahiptir.<\/li>\n<li><strong>So\u011fuk Ba\u015flatma Sorunu:<\/strong> Do\u011fru \u00f6nerilerde bulunmak i\u00e7in yeterli veri bulunmad\u0131\u011f\u0131ndan yeni kullan\u0131c\u0131lar veya yeni \u00f6\u011feler sorunlu olabilir; bu sorun, so\u011fuk ba\u015flatma sorunu olarak bilinir.<\/li>\n<\/ol>\n<h2>\u0130\u015fbirlik\u00e7i Filtreleme T\u00fcrleri<\/h2>\n<table>\n<thead>\n<tr>\n<th><strong>Tip<\/strong><\/th>\n<th><strong>Tan\u0131m<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Bellek tabanl\u0131 CF<\/td>\n<td>Kullan\u0131c\u0131lar\u0131n benzerli\u011fini veya \u00f6\u011felerin benzerli\u011fini hesaplamak i\u00e7in \u00f6nceki kullan\u0131c\u0131lar\u0131n etkile\u015fimlerinin haf\u0131zas\u0131n\u0131 kullan\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>Model tabanl\u0131 CF<\/td>\n<td>Model \u00f6\u011frenmenin bir ad\u0131m\u0131n\u0131 i\u00e7erir, ard\u0131ndan bu modeli tahminlerde bulunmak i\u00e7in kullan\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>Hibrit CF<\/td>\n<td>Baz\u0131 s\u0131n\u0131rlamalar\u0131n \u00fcstesinden gelmek i\u00e7in Bellek tabanl\u0131 ve Model tabanl\u0131 y\u00f6ntemleri birle\u015ftirir.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0130\u015fbirli\u011fine Dayal\u0131 Filtrelemeyi Kullanma: Zorluklar ve \u00c7\u00f6z\u00fcmler<\/h2>\n<p>CF, filmler, m\u00fczik, haberler, kitaplar, ara\u015ft\u0131rma makaleleri, arama sorgular\u0131, sosyal etiketler ve genel olarak \u00fcr\u00fcnler dahil ancak bunlarla s\u0131n\u0131rl\u0131 olmamak \u00fczere \u00e7e\u015fitli alanlarda yayg\u0131n kullan\u0131m alan\u0131 bulur. Ancak a\u015fa\u011f\u0131daki gibi zorluklar vard\u0131r:<\/p>\n<ol>\n<li><strong>So\u011fuk ba\u015flatma sorunu:<\/strong> \u00c7\u00f6z\u00fcm, i\u00e7eri\u011fe dayal\u0131 filtrelemeyi i\u00e7eren veya kullan\u0131c\u0131lar veya \u00f6\u011feler hakk\u0131nda ek meta veriler kullanan hibrit modellerde yatmaktad\u0131r.<\/li>\n<li><strong>K\u0131tl\u0131k:<\/strong> Bir\u00e7ok kullan\u0131c\u0131 az say\u0131da \u00f6\u011feyle etkile\u015fime girerek kullan\u0131c\u0131 \u00f6\u011fesi matrisini seyrek b\u0131rak\u0131r. Tekil de\u011fer ayr\u0131\u015ft\u0131rmas\u0131 gibi boyut azaltma teknikleri bu sorunu hafifletebilir.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik:<\/strong> Veriler b\u00fcy\u00fcd\u00fck\u00e7e \u00f6nerilerin h\u0131zl\u0131 bir \u015fekilde sa\u011flanmas\u0131 hesaplama a\u00e7\u0131s\u0131ndan yo\u011fun hale gelebilir. \u00c7\u00f6z\u00fcmler, da\u011f\u0131t\u0131lm\u0131\u015f hesaplamay\u0131 veya daha \u00f6l\u00e7eklenebilir algoritmalar\u0131n kullan\u0131lmas\u0131n\u0131 i\u00e7erir.<\/li>\n<\/ol>\n<h2>Benzer Tekniklerle Kar\u015f\u0131la\u015ft\u0131rma<\/h2>\n<table>\n<thead>\n<tr>\n<th><strong>Y\u00f6ntem<\/strong><\/th>\n<th><strong>Tan\u0131m<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u0130\u015fbirlik\u00e7i Filtreleme<\/td>\n<td>\u0130nsanlar\u0131n ge\u00e7mi\u015fte be\u011fendikleri \u015feylere benzer \u015feyleri ve benzer zevklere sahip ki\u015filerin be\u011fendi\u011fi \u015feyleri sevdi\u011fi varsay\u0131m\u0131na dayanmaktad\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>\u0130\u00e7erik Tabanl\u0131 Filtreleme<\/td>\n<td>\u00d6\u011felerin i\u00e7eri\u011fini ve kullan\u0131c\u0131n\u0131n profilini kar\u015f\u0131la\u015ft\u0131rarak \u00f6\u011feler \u00f6nerir.<\/td>\n<\/tr>\n<tr>\n<td>Hibrit Y\u00f6ntemler<\/td>\n<td>Bu y\u00f6ntemler, \u0130\u015fbirli\u011fine Dayal\u0131 Filtreleme ve \u0130\u00e7eri\u011fe Dayal\u0131 Filtrelemeyi birle\u015ftirerek belirli s\u0131n\u0131rlamalardan ka\u00e7\u0131nmay\u0131 ama\u00e7lamaktad\u0131r.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0130\u015fbirlik\u00e7i Filtrelemeye \u0130li\u015fkin Gelecek Perspektifleri<\/h2>\n<p>Daha karma\u015f\u0131k makine \u00f6\u011frenimi ve yapay zeka teknolojilerinin ortaya \u00e7\u0131kmas\u0131yla birlikte CF y\u00f6ntemleri de geli\u015fiyor. Art\u0131k CF i\u00e7in karma\u015f\u0131k modeller geli\u015ftirmek ve daha do\u011fru \u00f6neriler sa\u011flamak i\u00e7in derin \u00f6\u011frenme teknikleri kullan\u0131l\u0131yor. Ayr\u0131ca, veri seyrekli\u011fi ve so\u011fuk ba\u015flatma sorununun zorluklar\u0131n\u0131n ele al\u0131nmas\u0131na y\u00f6nelik ara\u015ft\u0131rmalar devam etmekte olup, gelecekte daha verimli ve etkili CF y\u00f6ntemleri vaat etmektedir.<\/p>\n<h2>Proxy Sunucular\u0131 ve \u0130\u015fbirli\u011fine Dayal\u0131 Filtreleme<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular\u0131, \u0130\u015fbirli\u011fine Dayal\u0131 Filtrelemeye dolayl\u0131 olarak yard\u0131mc\u0131 olabilir. Anonimlik ve g\u00fcvenlik sa\u011flayarak kullan\u0131c\u0131lar\u0131n gizlilik i\u00e7inde gezinmesine olanak tan\u0131rlar. Bu, kullan\u0131c\u0131lar\u0131 gizliliklerinden \u00f6d\u00fcn verme korkusu olmadan internetteki \u00f6\u011felerle \u00f6zg\u00fcrce etkile\u015fime girmeye te\u015fvik eder. Ortaya \u00e7\u0131kan veriler CF i\u00e7in \u00f6nemlidir, \u00e7\u00fcnk\u00fc \u00f6nerilerde bulunmak b\u00fcy\u00fck \u00f6l\u00e7\u00fcde kullan\u0131c\u0131 \u00f6\u011fesi etkile\u015fimlerine dayan\u0131r.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ol>\n<li><a href=\"http:\/\/grouplens.org\/\" target=\"_new\" rel=\"noopener nofollow\">GrupLens Ara\u015ft\u0131rmas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/research.netflix.com\/\" target=\"_new\" rel=\"noopener nofollow\">Netflix Ara\u015ft\u0131rmas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.science\/tag\/recommendation\" target=\"_new\" rel=\"noopener nofollow\">Amazon Ara\u015ft\u0131rmas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/dl.acm.org\/\" target=\"_new\" rel=\"noopener nofollow\">ACM Dijital K\u00fct\u00fcphanesi<\/a> \u0130\u015fbirlik\u00e7i Filtreleme \u00fczerine akademik ara\u015ft\u0131rma i\u00e7in<\/li>\n<li><a href=\"https:\/\/scholar.google.com\/\" target=\"_new\" rel=\"noopener nofollow\">Google Akademik<\/a> \u0130\u015fbirlik\u00e7i Filtreleme ile ilgili akademik makaleler i\u00e7in<\/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\/tr\/wp-json\/wp\/v2\/wiki\/476320","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476320\/revisions"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476320"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}