TensorIoT and Amazon Personalize
By: The TensorIoT Machine Learning Team
Have you ever looked something up online and gotten a suggested advertisement on your social media right after for a similar product? If the answer is yes, then you are familiar with what is otherwise known as a recommender system. Recommender systems are algorithms useful for for suggesting relevant items to users. These systems have become an integral part of businesses over the last few decades. From eCommerce, email marketing, inventory organization, etc. recommender systems have gained prime attention in improving customer engagement. Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications (aws.amazon.com).
There are two broad categories of recommender systems namely, content-based and collaborative-filtering based. Content-based recommender systems provide recommendations based on the similarity between the products such as providing music or video recommendations by genre. Whereas collaborative filtering-based recommendations consider a user’s attributes such as browsing history, page views, clicks, preferences, etc. to deliver the recommendations. Like those suggested advertisements we mentioned earlier. Since the user and item interaction space is very large, designing a robust recommender system at scale is complex for most organizations. This is where machine learning and Amazon Personalize comes in.
Machine learning is being widely used to improve customer engagement in many industries. Due to the complexity involved in building applications using machine learning being high, building sophisticated recommender systems has been beyond the reach of most of the organizations. With Amazon Personalize, you provide an activity stream from your application such as page clicks/views, registers, purchases, etc. or the items you want to recommend, such as — photos, articles, music etc. Additional metadata like demographic and psycho-graphic information can also be provided to Amazon Personalize. Once the data from the application is loaded into Amazon Personalize, it will inspect the data, identify potential features, perform algorithm selection, train and optimize a personalization model tailored to your application. All data analyzed by Amazon Personalize is kept private and secure and limited to use for your customized recommendations. These personalized recommendations can be invoked using a simple API named Customized Personalization API.
Amazon Personalize helps provide product and content recommendations tailored to a user’s preferences, behavior, and browsing history rather than offering a uniform experience to all users. This further contributes to an enhanced user experience. Thereby promoting customer satisfaction and engagement. Consider the example of a video streaming website, using Amazon Personalize: the website can help users discover additional videos that might be of interest to them by viewing it on their home screen. These recommendations will be unique to every user and require the recommendations to be provided at scale. Knowing the user as well as the content space is also a key factor. Adding videos similar to the ones viewed in the past by the individual user helps improve the user experience in finding similar content based on genre. Leveraging Amazon Personalize can incorporate collaborative filtering-based as well as content-based recommendations at scale in any application.
By leveraging Amazon Personalize, TensorIoT is able to offer our customers the benefits of delivering high-quality recommendations at scale. This allows our customers to see real-time predictions and offer customized and unique experience to all of their consumer. With a few clicks TensorIoT is contributing to enhanced customer engagement and conversion. Amazon Personalize automates and accelerates the machine learning process and developers need not have a prior machine learning experience to develop a sophisticated recommender system. This also limits time required in the initial infrastructure setup and experimentation during the development process in organizations. Also, due to the cost flexibility of the service, Amazon Personalize is a cost optimizing choice to be integrated with applications in organizations where recommender systems are a necessity.
