Developing A Recommender Solution With Azure Machine Learning

Introduction

Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Azure Machine Learning is a powerful cloud-based predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Azure Machine Learning not only provides tools to model predictive analytics but also provides a fully-managed service you can use to publish your predictive models as ready-to-consume web services. Read Predictive Analytics with Microsoft Azure Machine Learning for more details on the mechanisms of Azure Machine Learning. Scope This article shows how Azure Machine Learning can be used to develop a Recommender Solution. Ever wonder how websites like Amazon and eBay provide useful suggestions and recommendations? This article is for you! Designing the Experiment The following is the procedure to develop the experiment: Add the dataset In Azure Machine Learning, an existing dataset can be used or a new one can be loaded from an Azure Database, Azure Blob Storage, Data Feed Reader, Web Service or a Hive Query. In this example, the Movie Ratings Sample Data shall be used. The Movie Rating sample has the following columns: Exclude the columns that shall not be needed

To do so, the project columns tool object can be used. Add it to the experiment. Now, from the right menu, select the "launch column selector" to select the fields that shall be needed. Here, the timestamp column shall be excluded. Split the Data Now, the data shall be partitioned into 2 distinct sets: Train Data: Used to “train” the recommender. That is, the algorithm shall use this data to "learn" and make predictions. Test Data: Used to validate the results of the recommender Drag the split tool and connect it as in the following. Deciding the amount of data to use for training and testing is subjective. The ratio should be typed as a decimal number between 0 and 1 to represent the percentage of rows sent to the first output dataset. For example, if you type 0.75 as the value, the dataset would be split using the ratio 75:25, with 75% of the rows sent to the first output dataset, and 25% sent to the second output dataset. Add the Train Matchbox Recommender The Train, a recommendation model based on the Matchbox recommender engine. It has the ability to learn about people’s preferences from observing how they rate items such as movies, content or other products. This is where learning occurs. Add the Score Matchbox Recommender The Score Matchbox Recommender scores predictions for a dataset using the Matchbox recommender. It generates results based on a trained recommendation model.