:Use ALS to predict ratings of songs (old version)

ALS

ALS is a matrix factorization algorithm that factorizes sparse matrices and predicts the values of missing entries to obtain a basic training model. ALS is also known as a hybrid collaborative filtering algorithm that combines users and items.

In this topic, ratings of songs are used to show how ALS works. The source dataset, that is, Matrix A, contains the ratings that every user assigns to songs. The ratings may be sparse because not every user has listened to all the songs in the library, and not all the songs in the library are rated by every user. The following figure shows Matrix A. ALS factorizes Matrix A to the product of the transposes of Matrix X and Matrix Y.

Matrix A = Transpose of Matrix X × Transpose of Matrix Y

The columns in Matrix X and rows in Matrix Y are known as factors in ALS. Assume that Matrix X and Matrix Y contain three factors: personality, education level, and interest. The following figure shows Matrix X that is factorized from Matrix A. Matrix X contains Vector M of each user. The following figure shows Matrix Y that is factorized from Matrix A. Matrix Y contains Vector N of each song. Based on the factorized data, ratings can be predicted. For example, to predict the rating that User 6 assigns to Red Bean, you can multiply Vector M of User 6 in Matrix X by Vector N of Red Bean in Matrix Y.

Predict ratings of songs

1. Go to the Machine Learning Designer page.
   1. Log on to the Machine Learning Platform for AI console.
   2. In the left-side navigation pane, click Workspaces. On the Workspace list page, click the name of the workspace that you want to manage.
   3. In the left-side navigation pane, choose Model Training > Visualized Modeling (Designer) to go to the Machine Learning Designer page.
2. In the upper-right corner of the Visualized Modeling (Designer) page, click Go to Studio (Old Version).
3. Create and run an experiment.
   1. In the left-side navigation pane, click Home.
   2. In the Templates section, click Create below ALS music recommendations.
   3. In the New Experiment dialog box, configure the experiment parameters. You can use the default values of these parameters.

      Parameter	Description
      Name	The name of the experiment. Default value: ALS music recommendations.
      Description	The description of the experiment. Default value: Using ALS to implement music and movie related content recommendations.
      Save To	Select My Experiments.

   4. Click OK.
   5. Wait about 10 seconds and click Experiments in the left-side navigation pane.
   6. Click ALS music recommendations_XX under My Experiments. The canvas of the experiment appears.
      My Experiments is the directory for storing the experiment that you created and ALS music recommendations_XX is the name of the experiment. In the experiment name, _XX is the ID that the system automatically creates for the experiment.
   7. In the upper-left corner of the canvas, click Run.
      The system automatically configures the data source of the experiment and parameters of the ALS component. You can directly run the experiment. You can click each component on the canvas and view the component parameters in the right-side pane.
4. View the results of the experiment.
   1. After the experiment is run, right-click ALS Training-1 on the canvas and choose View Data > View Output Port 1. In the dialog box that appears, view the data of Matrix X.
   2. Right-click ALS Training-1 on the canvas and choose View Data > View Output Port 2. In the dialog box that appears, view the data of Matrix Y.
5. Predict ratings.
   To predict the rating that User 1 assigns to Item 978130429, multiply Vector M of User 1 in Matrix X by Vector N of Item 978130429 in Matrix Y.

   # Vector M 
   [0.5775652527809143,0.6737191677093506,0.9059759974479675,0.9866708517074585,0.15602371096611023,0.2735472023487091,0.4610620439052582,0.5312653183937073,0.3408969044685364,1.4993919134140015]
   # Vector N 
   [0.2431642860174179,0.6019538044929504,0.4035401940345764,0.254305899143219,0.4056856632232666,0.46871861815452576,0.3701469600200653,0.3757922947406769,0.26486095786094666,0.37488409876823425]