Amazon Personalize can now create up to 50% better recommendations for fast changing catalogs of new products and fresh content
Amazon Personalize now makes it easier to create personalized recommendations for fast-changing catalogs of books, movies, music, news articles, and more, improving recommendations by up to 50% (measured by click-through rate) with just a few clicks in the AWS console. Without needing to change any application code, Amazon Personalize enables customers to include completely new products and fresh content in their usual recommendations, so that the best new products and content is discovered, clicked, purchased, or consumed by end-users an order of magnitude more quickly than other recommendation systems.
Amazon Personalize now makes it easier to create personalized recommendations for fast-changing catalogs of books, movies, music, news articles, and more, improving recommendations by up to 50% (measured by click-through rate) with just a few clicks in the AWS console. Without needing to change any application code, Amazon Personalize enables customers to include completely new products and fresh content in their usual recommendations, so that the best new products and content is discovered, clicked, purchased, or consumed by end-users an order of magnitude more quickly than other recommendation systems.
Many catalogs are fast moving with new products and fresh content being continuously added, and it is crucial for businesses to help their users discover and engage with these products or content. For example, users on a news website expect to see latest personalized news, users consuming media via video-on-demand services expect to be recommended the latest series and episodes they might like. Meeting these expectations by showcasing new products and content to users helps keep the user experience fresh, and aids in sales either through direct conversion, or through subscriber conversion and retention. However, there are usually way too many new products in fast moving catalogs to make it feasible to showcase each of them to every user. It is more efficient to personalize the user experience by matching these new products with users, based on their interests and preferences. However, personalization of new products is inherently hard due to absence of data about past views, clicks, purchases, and subscriptions for these products. In such a scenario, most recommender systems only make recommendations for products they have sufficient past data about, and ignore the products that are new to the catalog.
With today’s launch, Amazon Personalize can help customers improve personalized recommendations for new products and fresh content for their users. Amazon Personalize does this by recommending new products to users who have positively engaged (clicked, purchased, etc.) with similar products in the past. Personalize learns more about these new items as users engage with them and automatically adjusts how frequently these items will be recommended to other similar users in future. At Amazon, such algorithms have long been used to create product recommendations, and has resulted in 21% higher conversions compared to recommendations that do not include new products.
This capability is now available in Amazon Personalize as a new algorithm (recipe) and can be easily used with a few clicks from the Amazon Personalize console, or using a simple API interface. To setup you first add data about your user, items, and the activity stream of users such as their clicks, purchases, and likes to Amazon Personalize, and then use the Amazon Personalize console or API to train a model (CreateSolution) using the new aws-user-personalization recipe. You can then get recommendations from Amazon Personalize for your users and can control the bias between recommending newer vs older items to them. To learn more about this feature, visit our blog.
This capability is available in Amazon Personalize in US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland), and Asia Pacific (Sydney, Tokyo, Mumbai, Singapore, Seoul).