AMAZON PERSONALIZE

From social media applications like Instagram and Twitter, to ecommerce websites like Amazon and Flipkart, to OTTs like Netflix and Hotstar, almost every platform online that has a range of items for the user to choose from, uses a recommendation system for the user to compensate for the array of items provided, quite often without the user even realizing it.

In this article, we have a look at how exactly Amazon Personalize works by being one such service that helps build recommendation systems- a simple but effective way to ensure that the user or consumer spends more time on your platform.

So, What is Amazon Personalize?

Amazon Personalize is a fully managed, Machine Learning (ML) based recommendation service which allows real time personalisation and recommendations to websites, applications, advertisements, emails and much more. Using Amazon Personalize helps generate relevant and personalised content for the user using a simple API interface.

As consumers become increasingly habituated with dynamic experiences for products they use, the best way to meet this demand is by using data to ensure hyper-personalized experiences, as you may witnessed while personally using certain applications. Incorporating personalization and recommendations are ways to build strong customer relationships and yield robust consumer insights.

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What can you do with Amazon Personalize???

1) Personalized, AI-backed consumer experience without ML proficiency

2) Provide personalized recommendations to consumers based on their purchase behaviour

3) Solve problems like cold starts and address popularity biases

4) Enhance user intent

5) Create custom marketing campaigns, offers, and tailored results for consumers Improve conversion rate by deploying real-time user activity data

6) Accurate recommendations

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How does Amazon Personalize work?

A combination of hyper-parameters, along with a learning algorithm forms the base of Amazon Personalize, referred to as a recipe. The learning algorithm could be content-based, or it could work via collaborative filtering, or even a combination of the two.

Amazon Personalize requires 3 forms of information, or three datasets to build a recommendation system.

1) Users: This dataset stores metadata about your users. This might include information such as age, gender, along with the users' unique ID.

2) Items: This dataset stores metadata about your items. This might include information such as price, categories, list of movies, products, songs, etc.

3) Interactions: This dataset stores historical and real-time data from interactions between users and items to be analysed and acted upon.

For Amazon Personalize, items and users contain metadata. This information could be used for some recipes. Depending on the recipe, this metadata could be more useful or not. The metadata could include external information related to demographics, in the case of users, and item behaviour, in the case of items. The interactions represent all the operations that users have performed on the data.

Despite the advantages of this service, adopting machine learning may get difficult and time-consuming without appropriate results. Below, there are mentioned some necessary steps to be taken in order to get the most out of Amazon Personalize.

1) Dataset Group: A dataset group is a must within Personalize, it will utilize imported datasets, and those datasets will be used by the model for training.

2) Schema & Dataset Import job: The data schema must be defined by the developer, and the schema must match with the data. The data schema refers to the skeleton structure of the entire database, which provides structure and logic to the database. After defining schema, the next step would be the data import process to import data from Amazon S3 buckets into personalize dataset group.

3) Solution & Recipe: A Solution refers to the combination of an Amazon Personalize recipe, customized parameters, and one or more solution versions- which are trained models based on ML. A recipe is an Amazon Personalize term specifying an appropriate algorithm to train.

4) Campaign: A campaign is used to deploy a trained solution version to get a recommendation. A campaign is a deployed solution version with provisioned dedicated transaction capacity for creating real time recommendations for users.

5) Get Recommendation: APIs are used to get real time recommendation from the campaign, and batch inference (based on batch of observations) to get a batch recommendation.

6) Data Incrementation: To retrain a model on new data, data needs to be added to the Personalize dataset group.

7) Events: To increments interaction data in real time, events can be created and used to insert new interaction data.

8) User & Item Incrementation: APIs are available to increase User and Item data as well.

9) Filtering: Filtering is used to filter the recommendation result. Using a filter, recommendations can be customised as well. For example- a filter could be inserted to hide items that have already been consumed by the user.

Examples

Now that we have had a look at how exactly Amazon Personalize functions, we will have a look at a few examples of how Personalize has benefited companies that use the service.

1) Bundesliga:?Bundesliga is Germany’s premier football league. The league's official application leverages Amazon Personalize to create an individualized, regionalized and personalized experience for their fans.They used Amazon Personalize to generate individualized content for our millions of active Bundesliga Official Application users each season. As a result, they have seen a 67% increase in article reads per user and a 17% improvement in the amount of time users spend in the app. Amazon Personalize is instrumental in supplying their fans with the content they want to see more effectively.

2) Coursera: Coursera is a leading provider of universal access to the world’s best education, partnering with over 190 top universities and organizations to offer courses online to its more than 40 million users. With over 4,000 classes available on Coursera's platform, the challenge is tailoring the experience to the personal interests of every user. Amazon Personalize allows them to adapt to individual preferences in real-time, providing highly relevant recommendations that engage their learners. Within a few weeks they were able to develop and deploy the Amazon Personalize model into production with the benefits of automatically scaling for their 40 million users.

3) Intuit: Intuit is a business and financial software company that develops and sells financial, accounting and tax preparation software and related services for small businesses, accountants and individuals. With Amazon Personalize, they were able to quickly design and launch a recommendation engine for Intuit’s Mint budget tracker and planner app. Using customer profile and behavioural data, with machine learning, the service helped their platform deliver the right financial offer to the right customer at the right time, based on their spending habits, lifestyle, and goals.

How can Ataloud help?

Connect with an Ataloud consultant today ([email protected]) for a seamless experience for your business' transition to the cloud. We can analyse, discuss and help validate your AWS billing and usage patterns, perform routine audits, perform log analysis, analyse and monitor performances- on top of the other managed services that we offer.?