{"id":1267,"date":"2021-11-29T08:29:37","date_gmt":"2021-11-29T08:29:37","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/29\/amazon-personalize-announces-recommenders-optimized-for-retail-and-media-entertainment\/"},"modified":"2021-11-29T08:29:37","modified_gmt":"2021-11-29T08:29:37","slug":"amazon-personalize-announces-recommenders-optimized-for-retail-and-media-entertainment","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/29\/amazon-personalize-announces-recommenders-optimized-for-retail-and-media-entertainment\/","title":{"rendered":"Amazon Personalize announces recommenders optimized for Retail and Media &amp; Entertainment"},"content":{"rendered":"<div id=\"\">\n<p>Today, we\u2019re excited to announce the launch of personalized recommenders in <a href=\"https:\/\/aws.amazon.com\/personalize\/\" target=\"_blank\" rel=\"noopener noreferrer\">Amazon Personalize<\/a> that are optimized for retail and media and entertainment, making it even easier to personalize your websites, apps, and marketing campaigns. With this launch, we have drawn on Amazon\u2019s rich experience creating unique personalized user experiences using machine learning (ML) to build recommenders for common personalization use cases. Use cases optimized recommendation solutions deliver personalized experiences for your users that consider the metrics that matter most to your business, the preferences of your individual users, and where your users are being served a personalized experience within the user journey. You can quickly integrate recommenders into any application via easy-to-use APIs.<\/p>\n<p>This post walks you through the process of creating a recommender and getting recommendations for your users.<\/p>\n<h2>New personalized recommenders<\/h2>\n<p>To realize the true potential of personalization, businesses need to tailor their content to the user journey. For instance, an ecommerce website can recommend products to an existing customer based on their past browsing history (for example, a \u201cRecommended for you\u201d carousel) to drive greater engagement by providing item recommendations that are relevant to that user\u2019s individual interests. On a product detail page, you can upsell products through a \u201cCustomers who viewed X also viewed\u201d widget that uses the context of the product your customer is already engaging with. Finally, on the checkout page, a retailer may want to cross-sell products with \u201cFrequently bought together\u201d recommendations to increase average order value.<\/p>\n<p>Similarly, a video-on-demand business can place a widget on their home page that shows the most popular recommendations to highlight the most viewed content across the world in the past week or month. You may want to build a \u201cBecause you watched this\u201d widget after videos are watched to provide similar content with a greater chance of driving an increase in the time spent on your platform.<\/p>\n<p>Each touchpoint requires intelligent personalization that understands the user, their current context, and their real-time interests or in-session preferences when delivering recommendations. Businesses today understand the need for and benefits of personalization, but building recommendation systems from the ground up requires significant investments of time and resources, in addition to extensive ML expertise.<\/p>\n<p>With the launch of recommenders, you simply select the use cases you need from a library of recommenders within Amazon Personalize. \u201cMost Viewed,\u201d \u201cBest Sellers\u201d, \u201cFrequently Bought Together,\u201d \u201cCustomers who Viewed X also Viewed,\u201d and \u201cRecommended for you\u201d are available for retail, and \u201cMost Popular,\u201d \u201cBecause you Watched X,\u201d \u201cMore Like X,\u201d and \u201cTop Picks\u201d are available for media and entertainment, with more to come. You select the recommenders for your use cases and Amazon Personalize does the heavy lifting of using ML to generate recommendations that you access through an easy-to-use API.<\/p>\n<p>Recommenders learn from your users\u2019 historical activity as well as their real-time interactions with items in your catalog to adjust to changing user preferences and deliver immediate value to your end users and business. Recommenders fully manage the lifecycle of maintaining and hosting personalized recommendation solutions. This accelerates the time needed to bring a solution to market and ensures that the recommendation solutions you deliver to production stay relevant for your users.<\/p>\n<p>Amazon Personalize enables developers to build personalized user experiences with the same ML technology used by Amazon with no ML expertise required. We make it easy for developers to build applications capable of delivering a wide array of personalization experiences. You can start getting recommendations with Amazon Personalize quickly using a few simple API calls or some clicks on the <a href=\"http:\/\/aws.amazon.com\/console\" target=\"_blank\" rel=\"noopener noreferrer\">AWS Management Console<\/a>. You only pay for what you use, with no minimum fees or upfront commitments. All data is encrypted to be private and secure, and is only used to create your recommendations and segments.<\/p>\n<h2>Create a recommender<\/h2>\n<p>This section walks through the process of creating a recommender. The first step is to create a domain dataset group, which you can create by loading historic data in <a href=\"http:\/\/aws.amazon.com\/s3\" target=\"_blank\" rel=\"noopener noreferrer\">Amazon Simple Storage Service<\/a> (Amazon S3) or from data gathered from real-time events.<\/p>\n<p>Each dataset group can contain up to three datasets: Users, Items, and Interactions, with the Interactions dataset being mandatory to create a recommender. Datasets must adhere to the domain-specific schema in order to be used to create the domain-related recommenders.<\/p>\n<p>In this post, we use the Amazon Prime Pantry dataset, which consists of purchase-related data for grocery items, to set up a retail recommender. We have uploaded the interactions dataset under the dataset group <code>Prime-Pantry<\/code>. You can monitor the status of the data upload through the dashboard for the <code>Prime-Pantry<\/code> dataset group on the Amazon Personalize console. After the data is imported successfully, choose <strong>Create recommenders<\/strong>.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-31242\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2021\/11\/24\/1-6938.jpg\" alt=\"\" width=\"800\" height=\"406\"><\/p>\n<p>As of this writing, Amazon Personalize offers five recipes for retail customers and four for media and entertainment customers.<\/p>\n<p>The retail recipes are as follows:<\/p>\n<ul>\n<li><strong>Customers who viewed X also viewed<\/strong> \u2013 Recommendations for items that customers also viewed when they viewed a given item<\/li>\n<li><strong>Frequently bought together<\/strong> \u2013 Recommendations for items that customers buy together based on a specific item<\/li>\n<li><strong>Popular Items by Purchases<\/strong> \u2013 Popular items based on the items purchased by your users<\/li>\n<li><strong>Popular Items by Views<\/strong> \u2013 Popular items based on items viewed by your users<\/li>\n<li><strong>Recommended for you<\/strong> \u2013 Personalized recommendations for a given user ensuring that any items previously purchased are filtered out<\/li>\n<\/ul>\n<p>The recipes for media and entertainment are as follows:<\/p>\n<ul>\n<li><strong>Most Popular<\/strong> \u2013 Most popular videos<\/li>\n<li><strong>Because you watched X<\/strong> \u2013 Videos similar to a given video watched by a user<\/li>\n<li><strong>More like X<\/strong> \u2013 Videos similar to a given video<\/li>\n<li><strong>Top picks for you<\/strong> \u2013 Personalized content recommendations for a specified user<\/li>\n<\/ul>\n<p>The following screenshot shows how you can select recommenders based on your business needs and define the names of the recommenders. You use each recommender\u2019s ARN to get recommendations when using the REST APIs. In this example, we create two recommenders. The first recommender is for the use case \u201cItems frequently bought together\u201d and is called <code>PP-ItemsFrequentlyBoughtTogether<\/code>. We also create a recommender for the use case \u201cPopular Items by Purchases\u201d called <code>PP-PopularItemsByPurchases<\/code>.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-31243\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2021\/11\/24\/2-6938.jpg\" alt=\"\" width=\"800\" height=\"1053\"><\/p>\n<p>You can toggle <strong>Use default recommender configurations <\/strong>and Amazon Personalize automatically chooses the best configuration for the models underlying the recommenders. Then choose <strong>Create recommenders<\/strong> to start the model building process.<\/p>\n<p>The time taken to create a recommender depends on the data and use cases selected. During this time, Amazon Personalize selects the optimal algorithm for each of the selected use cases, processes the underlying data, and trains a custom private model for your users. You can access all your recommenders and their current status on the <strong>Recommenders <\/strong>page.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-31244\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2021\/11\/24\/3-6938.jpg\" alt=\"\" width=\"800\" height=\"192\"><\/p>\n<p>When the recommender\u2019s status changes to <code>Active<\/code>, you can choose it to review relevant details about the recommender and test it. Testing helps check the recommendations before you integrate the recommender into your website or application.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-31245\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2021\/11\/24\/4-6938.jpg\" alt=\"\" width=\"800\" height=\"340\"><\/p>\n<p>The following image shows the test output for a particular item ID for the recommender <code>PP ItemsFrequentlyBoughtTogether<\/code>.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-31246\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59\/2021\/11\/24\/5-6938.jpg\" alt=\"\" width=\"800\" height=\"443\"><\/p>\n<p>At this step, you can also apply any filters on the recommendations; for example, to remove items purchased in the past.<\/p>\n<p>Amazon Personalize also provides a recommender ARN in the details section, which you can use to produce recommendations through the Amazon Personalize REST APIs. The following code is an example of calling your API from Python for <code>PP-FrequentlyBoughtTogetherRecommender<\/code>:<\/p>\n<div class=\"hide-language\">\n<pre class=\"unlimited-height-code\"><code class=\"lang-bash\">get_recommendations_response = personalize_runtime.get_recommendations( \ncampaignArn = arn:aws:personalize:us-west-2:261294318658:recommender\/PP-ItemsFrequentlyBoughtTogether \nitemId = str(item_id) \n)\n<\/code><\/pre>\n<\/p><\/div>\n<p>This API call produces the same results as if testing the recommender via the console.<\/p>\n<p>Your recommender is now ready to feed into your website or app and personalize the journey of each of your customers.<\/p>\n<h2>Conclusion<\/h2>\n<p>Amazon Personalize packages our rich experience creating unique personalized user experiences with ML at Amazon and offers our expertise as a fully managed service to developers looking to personalize their websites and apps. With the launch of use case optimized recommenders, we\u2019re going one step further to tailor our learnings to the unique marketing needs of each industry and each individual business. Recommenders allow you to easily and swiftly access recommendations that are optimized for your specific use case. By understanding the unique context of your customers and their touchpoints, Amazon Personalize allows you to harness the raw power of ML to derive more value for your business and your users.<\/p>\n<p>To learn more about Amazon Personalize, visit the <a href=\"https:\/\/aws.amazon.com\/personalize\/\" target=\"_blank\" rel=\"noopener noreferrer\">product page<\/a>.<\/p>\n<hr>\n<h3>About the Authors<\/h3>\n<p><strong>Anchit Gupta<\/strong> is a Senior Product Manager for Amazon Personalize. She focuses on delivering products that make it easier to build machine learning solutions. In her spare time, she enjoys cooking, playing board\/card games, and reading.<\/p>\n<p><strong>Hao Ding<\/strong> is an Applied Scientist at AWS AI Labs and is working on developing next generation recommender system for Amazon Personalize. His research interests include Recommender System, Deep Learning, and Graph Mining.<\/p>\n<p><strong>Pranav Agarwal<\/strong> is a Sr. Software Development Engineer with Amazon Personalize and works on architecting software systems and building AI-powered recommender systems at scale. Outside of work, he enjoys reading, running and has started picking up ice-skating.<\/p>\n<p><strong>Nghia Hoang<\/strong> is a Senior Machine Learning Scientist at AWS AI Labs working on developing personalized learning methods with applications to recommender systems. His research interests include Probabilistic Inference, Deep Generative Learning, Personalized Federated Learning and Meta Learning.<\/p>\n<p>       <!-- '\"` -->\n      <\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/aws.amazon.com\/blogs\/machine-learning\/amazon-personalize-announces-recommenders-optimized-for-retail-and-media-entertainment\/<\/p>\n","protected":false},"author":0,"featured_media":1268,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/1267"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=1267"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/1267\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/1268"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=1267"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=1267"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=1267"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}