Azure Personalizer

Azure Personalizer is a cloud-based API service that helps developers create rich, personalized experiences for each user of your app. It learns from customer's real-time behavior, and uses reinforcement learning to select the best item (action) based on collective behavior and reward scores across all users. Actions are the content items, such as news articles, specific movies, or products. It takes a list of items (e.g. list of drop-down choices) and their context (e.g. Report Name, User Name, Time Zone) as input and returns the ranked list of items for the given context. While doing that, it also allows feedback submission regarding the relevance and efficiency of the ranking results returned by the service. The feedback (reward score) can be automatically calculated and submitted to the service based on the given personalization use case.

You can use the Personalizer service to determine what product to suggest to shoppers or to figure out the optimal position for an advertisement. After the content is shown to the user, your application monitors the user's reaction and reports a reward score back to the Personalizer service. This ensures continuous improvement of the machine learning model, and Personalizer's ability to select the best content item based on the contextual information it receives.

Content is any unit of information, such as text, images, URL, emails, or anything else that you want to select from and show to your users. It is highly essential to come up with well thought out features that represent the given items and their context most effectively as per the objective of personalization use case. Exploration ensures the Personalizer continues to deliver good results even in the changing user behavior and avoids model stagnation, drift, and ultimately lower performance. Learning policy determines the specific hyperparameters for the model training. These can be optimized offline (using offline evaluation) and then used online. These can be imported/exported for future reference, re-use, and audit. Personalizer starts with default learning policy which can yield moderate performance. As part of optimization, Evaluations are run that allows Personalizer to create new Learning Policies specifically optimized to a given use case. Optimized learning policies perform significantly better for each specific loop, generated during evaluation. Don’t use Personalizer where the personalized behavior isn’t something that can be discovered across all users but rather something that should be remembered for specific users or comes from a user-specific list of alternatives.

Personalizer Service can return a rank very rapidly, and azure will auto-scale on need basis to maintain the rapid generation of ranking results. Throughput is calculated by adding the size of action and context JSON documents, and factor the rate of 20 MB / sec. Context and Actions (items) are expressed as a JSON object that is sent with the Rank API call. JSON objects can include nested JSON objects and simple property/values. Arrays can be included if the items are numbers. Once embedded, Personaliser can skip the learning curve with apprentice mode. When the feature is switched on, the service learns alongside your existing solution without being exposed to users until it meets your performance threshold.

Following are some of the interesting use cases of Azure Personalizer: