{"id":186092,"date":"2024-06-12T20:10:52","date_gmt":"2024-06-12T19:10:52","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=186092"},"modified":"2024-06-12T20:10:54","modified_gmt":"2024-06-12T19:10:54","slug":"all-about-recommendation-algorithm","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-recommendation-algorithm","title":{"rendered":"Recommendation algorithm: What is it? How does it work?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"186092\" class=\"elementor elementor-186092\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-439a3ea e-flex e-con-boxed e-con\" data-id=\"439a3ea\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-266d016 elementor-widget elementor-widget-heading\" data-id=\"266d016\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">When YouTube recommends videos that match our current interests, or when Amazon suggests products we might find intriguing, what mechanisms are at play? Recommendation algorithms. These are highly intricate systems developed to further personalize the user experience, though with the potential risk of producing sometimes undesirable effects of polarization. They also spark debates regarding the extensive cross-referencing of our personal data from various sources...<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-dd77ad4 e-flex e-con-boxed e-con\" data-id=\"dd77ad4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4cb316c elementor-widget elementor-widget-text-editor\" data-id=\"4cb316c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">As companies like Amazon, Google, or Facebook ventured into the internet sector and established their dominance where tens of thousands of startups had faltered, they encountered a specific challenge: How to keep a visitor engaged? How do they ensure that an individual is enticed to revisit, and in the case of news or streaming platforms, to remain engaged for as long as possible?<\/span><\/p><p><span style=\"font-weight: 400;\">In navigating this uncharted territory, <\/span><b>the tech giants<\/b><span style=\"font-weight: 400;\"> devised innovative strategies to captivate visitors through the use of recommendation algorithms.<\/span><\/p><p><span style=\"font-weight: 400;\">These algorithms were formulated with several goals in mind:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">to make the user experience as enjoyable as possible;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">to present visitors with content that aligns closely with their interests;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">to improve the performance of various key metrics (such as video viewing time, reading duration, average shopping cart value, etc.);<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">to filter content in a manner that is customized for each individual.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-12d67fa e-flex e-con-boxed e-con\" data-id=\"12d67fa\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-adee73e elementor-widget elementor-widget-text-editor\" data-id=\"adee73e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The crucial keyword here is: <\/span><b>personalization<\/b><span style=\"font-weight: 400;\">. For a media company like Facebook, achieving this is quite remarkable. Over <\/span><b>two billion users experience a unique news feed that is constantly refreshed<\/b><span style=\"font-weight: 400;\"> every second.<\/span><\/p><p><span style=\"font-weight: 400;\">Consider YouTube as another example. As the leading online video platform, it houses billions of videos and is updated with new content daily. Nonetheless, upon logging into YouTube, you are presented with merely a few dozen video recommendations, a selection that changes with each visit but consistently \u2013 albeit not always subtly \u2013 aims to <\/span><b>provide a lineup that will grab your attention<\/b><span style=\"font-weight: 400;\">. How does YouTube manage to offer content that makes you eager to return and explore more? Primarily by <\/span><b>merging topics<\/b><span style=\"font-weight: 400;\"> that you find interesting at any given moment with those of other users who share a similar profile to yours.<\/span><\/p><p><span style=\"font-weight: 400;\">All the major digital entities quickly realized the potential of tapping into the vast data reservoir of internet users. Today, these <a href=\"https:\/\/datascientest.com\/en\/artificial-intelligence-definition\">recommendation algorithms<\/a> are employed by a large array of websites. They are geared towards identifying the most suitable new content at any given time. Among the most familiar examples are:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the post or friend suggestions on Facebook, Instagram, or Twitter;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the products recommended by Amazon or Alibaba;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the video clips suggested by YouTube or TikTok;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the featured articles on news portals;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the travel destinations and activities proposed by sites like Booking and TripAdvisor.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c3fdc9 elementor-widget elementor-widget-text-editor\" data-id=\"5c3fdc9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The success of this approach is undeniable. So much so, that at Netflix, <\/span><b>80% of the films watched the most originate from recommendation algorithms<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-232a609 elementor-widget elementor-widget-image\" data-id=\"232a609\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"457\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/06\/recommandation-algorithm1.jpg\" class=\"attachment-full size-full wp-image-186101\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2024\/06\/recommandation-algorithm1.jpg 800w, https:\/\/datascientest.com\/en\/files\/2024\/06\/recommandation-algorithm1-300x171.jpg 300w, https:\/\/datascientest.com\/en\/files\/2024\/06\/recommandation-algorithm1-768x439.jpg 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3fa814 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"d3fa814\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn all about algorithms<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4e5bce3 e-flex e-con-boxed e-con\" data-id=\"4e5bce3\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8c2e7ec elementor-widget elementor-widget-heading\" data-id=\"8c2e7ec\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do recommendation algorithms work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a53ea9b elementor-widget elementor-widget-text-editor\" data-id=\"a53ea9b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">How do these internet behemoths fine-tune their approach to target each of us with increasing accuracy? Their recommendation algorithms utilize filtering methods to recognize patterns. The principal strategies they employ are as follows.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-26f58e6 elementor-widget elementor-widget-heading\" data-id=\"26f58e6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Collaborative Filtering<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84428be elementor-widget elementor-widget-text-editor\" data-id=\"84428be\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Collaborative filtering stands as one of the most employed and effective recommendation algorithms. It operates on the theory that if two individuals have appreciated similar content in the past, they will likely enjoy the same content in the future.<\/span><\/p><p><span style=\"font-weight: 400;\">The strength of collaborative filtering lies in its <\/span><b>complete lack of directiveness<\/b><span style=\"font-weight: 400;\">. It relies entirely on the user&#8217;s history. Yet, this method has the drawback of diminishing the diversity of content accessible to the user. Users end up being exposed not to a variety of perspectives but predominantly to information that echoes their prevailing beliefs.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e18fb2 elementor-widget elementor-widget-heading\" data-id=\"7e18fb2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Content-based Filtering<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2097628 elementor-widget elementor-widget-text-editor\" data-id=\"2097628\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The content-based technique analyses a collection of content whilst disregarding the preferences of other users. It concentrates on <\/span><b>similarities to recommend content<\/b><span style=\"font-weight: 400;\">. The essence of content is determined by cataloging key words and then comparing these to the words in other pieces of content.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d394a0 elementor-widget elementor-widget-heading\" data-id=\"2d394a0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Popularity<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-26e5583 elementor-widget elementor-widget-text-editor\" data-id=\"26e5583\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">This algorithm presumes that if you frequently visit a specific website, you are likely to enjoy the content that attracts the most visitors. In essence, <\/span><b>it recommends the most popular content<\/b><span style=\"font-weight: 400;\">. An advantage of this technique is that it can be applied to new users of a site.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ea78853 e-flex e-con-boxed e-con\" data-id=\"ea78853\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dd30efb elementor-widget elementor-widget-heading\" data-id=\"dd30efb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The risks of polarization<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d10955 elementor-widget elementor-widget-text-editor\" data-id=\"2d10955\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">While recommendation algorithms provide numerous benefits to users through personalized recommendations that are frequently relevant, they can also present several disadvantages, some of which have <\/span><b>societal implications<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, there has been a notable increase in the number of individuals expressing extreme views on subjects such as politics or climate change. This phenomenon of &#8220;polarization&#8221; can appear dangerous, as it has the potential to <\/span><b>erode critical thinking<\/b><span style=\"font-weight: 400;\"> or, more simply, the ability to explore beyond conventional paths.<\/span><\/p><p><span style=\"font-weight: 400;\">Similarly, SACEM disclosed that 99% of the most streamed tracks on <a href=\"https:\/\/open.spotify.com\/\">Spotify<\/a> pertain to just 10% of the catalog. Even more concerning is that 20% of the tracks are never suggested to listeners.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dc841bd elementor-widget elementor-widget-image\" data-id=\"dc841bd\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"457\" src=\"https:\/\/datascientest.com\/en\/files\/2024\/06\/recommandation-algorithm2.jpg\" class=\"attachment-full size-full wp-image-186108\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-708f7e2 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"708f7e2\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our  courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba22191 elementor-widget elementor-widget-html\" data-id=\"ba22191\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What is a recommendation algorithm?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Recommendation algorithms are highly intricate systems developed to further personalize the user experience, though with the potential risk of producing sometimes undesirable effects of polarization.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Why recommendation algorithms are important?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"All the major digital entities quickly realized the potential of tapping into the vast data reservoir of internet users. Today, these recommendation algorithms are employed by a large array of websites. They are geared towards identifying the most suitable new content at any given time.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"How a recommendation algorithm works?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"How do these internet behemoths fine-tune their approach to target each of us with increasing accuracy? Their recommendation algorithms utilize filtering methods to recognize patterns. The principal strategies they employ are as follows: \\n - Collaborative Filtering \\n - Content-based Filtering \\n - Popularity\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>When YouTube recommends videos that match our current interests, or when Amazon suggests products we might find intriguing, what mechanisms are at play? Recommendation algorithms. These are highly intricate systems developed to further personalize the user experience, though with the potential risk of producing sometimes undesirable effects of polarization. They also spark debates regarding the [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":186094,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Recommendation algorithm: What is it? How does it work?<\/title>\n<meta name=\"description\" content=\"When YouTube recommends videos that address our current concerns, when Amazon suggests items that may interest us, what is at work? 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