Amazon Personalize now enables you to optimize personalized recommendations for a business metric of your choice, in addition to improving relevance of recommendations for your users. You can define a business metric such as revenue, profit margin, video watch time, or any other numerical attribute of your item catalog to optimize your recommendations. Amazon Personalize automatically learns what is relevant to your users, considers the business metric you’ve defined, and recommends the products or content to your users that benefit your overall business goals. Configuring an additional objective is easy. You select any numerical column in your catalog when creating a new solution in Amazon Personalize via the AWS Management Console or the API, and you’re ready to go.
Amazon Personalize enables you to easily add real-time personalized recommendations to your applications without requiring any ML expertise. With Amazon Personalize, you pay for what you use, with no minimum fees or upfront commitments. You can get started with a simple three-step process, which takes only a few clicks on the console or a few simple API calls. First, point Amazon Personalize to your user data, catalog data, and activity stream of views, clicks, purchases, and so on, in Amazon Simple Storage Service (Amazon S3) or upload using an API call. Second, either via the console or an API call, train a custom, private recommendation model for your data (CreateSolution). Third, retrieve personalized recommendations for any user by creating a campaign and using the GetRecommendations API.
The rest of this post walks you through the suggested best practices for generating recommendations for your business in greater detail.
Streaming movie service use case
In this post, we propose a fictitious streaming movie service, and as part of the service we provide movie recommendations using movie reviews from the MovieLens database. We assume the streaming service’s agreement with content providers requires royalties every time a movie is viewed. For our use case, we assume movies that have royalties that range from $0.00 to $0.10 per title. All things being equal, the streaming service wants to provide recommendations for titles that the subscriber will enjoy, but minimize costs by recommending titles with lower royalty fees.
It’s important to understand that a trade-off is made when including a business objective in recommendations. Placing too much weight on the objective can lead to a loss of opportunities with customers as the recommendations presented become less relevant to user interests. If the objective weight doesn’t impart enough impact on recommendations, the recommendations will still be relevant but may not drive the business outcomes you aim to achieve. By testing the m
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/optimize-personalized-recommendations-for-a-business-metric-of-your-choice-with-amazon-personalize/