{"id":12742,"date":"2018-10-23T00:00:00","date_gmt":"2018-10-23T00:00:00","guid":{"rendered":"https:\/\/e-dialog.group\/blog\/recommendation-systems-with-google-analytics-raw-data\/"},"modified":"2026-02-26T13:38:05","modified_gmt":"2026-02-26T13:38:05","slug":"recommendation-systems-with-google-analytics-raw-data","status":"publish","type":"post","link":"https:\/\/e-dialog.group\/en\/blog\/recommendation-systems-with-google-analytics-raw-data\/","title":{"rendered":"Recommendation Systems With Google Analytics Raw Data"},"content":{"rendered":"<h2>Use BigQuery data with machine learning<\/h2>\n<p>Suggesting visitors the articles that are <strong>relevant to them<\/strong> in order to offer a <strong>better user experience<\/strong> is a core goal for publishers. On the one hand, this makes users happier because they receive article recommendations that actually interest them. On the other hand, the publisher is happy about a <strong>higher engagement rate<\/strong> and <strong>dwell time<\/strong> and therefore also about <strong>higher advertising sales<\/strong>.<\/p>\n<p>With these thoughts in mind, we have developed an article recommendation engine that uses <strong>machine learning<\/strong> to provide visitors with the articles that are relevant to them as recommendations. It works on the assumption that visitors reading similar articles have the same preferences. Based on user behavior, articles that similar visitors have viewed are recommended.<\/p>\n<h2>Build or Buy &ndash; that already exists<br>\n<\/h2>\n<p>Article recommendations already exist from many providers, <strong>but from the publisher&rsquo;s perspective these are a black box<\/strong>. You pass on valuable user data and do not have full control. This means that <strong>insourcing with full data sovereignty<\/strong> is a significant advantage from both a strategic and compliance perspective (GDPR). The individual design and optimization options are also an important asset.<\/p>\n<h2>The implementation<br>\n<\/h2>\n<p>The project was implemented in <strong>collaboration with Google and Kurier,<\/strong> one of the largest Austrian publishers. The basis for the article recommendations is the valuable raw data from Google Analytics 360. Furthermore, the user data is exported to Google&rsquo;s data warehouse via the native BigQuery integration. Various cloud services are available there that can be used to implement machine learning projects.<\/p>\n<p>The following figure illustrates which services we use for our solution.<\/p>\n<p>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"396\" src=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2025\/08\/Recommender-Systems.jpg\" alt=\"Recommender-Systems\" loading=\"lazy\" srcset=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2025\/08\/Recommender-Systems.jpg 1024w, https:\/\/e-dialog.group\/wp-content\/uploads\/2025\/08\/Recommender-Systems-300x149.jpg 300w, https:\/\/e-dialog.group\/wp-content\/uploads\/2025\/08\/Recommender-Systems-768x380.jpg 768w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\">\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/p>\n<h2>TensorFlow for machine learning algorithms<br>\n<\/h2>\n<p>We created the model with <strong>TensorFlow<\/strong>, Google&rsquo;s framework for machine learning algorithms. Our approach relies on a method called <strong>&ldquo;collaborative filtering&rdquo;<\/strong>. On the one hand, we assume that visitors with similar user behavior share common interests. On the other hand, we assume that this will be reflected in the behavior of users and that they will access similar articles.<\/p>\n<p>What we are already doing today is optimizing the average session duration. In the future, we plan to include additional key figures, such as the <strong>Article Teaser Click Through Ratio and the advertising revenue.<\/strong><\/p>\n<p>The current approach, the <strong>&ldquo;collaborative-filtering&rdquo; <\/strong>approach, requires that user data already exists. In order to be able to provide new visitors with recommendations right from the start, we would also like to pursue a &ldquo;content-based&rdquo; approach. The fallback solution is to recommend similar articles if user data is not available.<\/p>\n<h3>Do you still have questions about the Article Recommendation Engine or would you like to tackle a Google Cloud project with us? Please feel free to contact us at <a href=\"mailto:kontakt@e-dialog.group\">kontakt@e-dialog.group<\/a><br>\n<\/h3>\n","protected":false},"excerpt":{"rendered":"<p>Article recommendation engines enable publishers to offer their users the best possible user experience on their own website. Together with Google, we have developed article recommendations via machine learning with TensorFlow and present all the details in this blog post.<\/p>\n","protected":false},"author":1,"featured_media":4804,"comment_status":"closed","ping_status":"open","sticky":false,"template":"post-old.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5,445],"channel":[],"goal":[463],"technology":[36],"c-year":[64],"class_list":{"0":"post-12742","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-analytics","9":"goal-digital-strategy","10":"technology-google-analytics","11":"c-year-64"},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Recommendation Systems With Google Analytics Raw Data - e-dialog<\/title>\n<meta name=\"description\" content=\"Article recommendation engines enable publishers to offer their users the best possible user experience on their own website. 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