Recommend products based on object features

Dataset

The experiment described in this topic is based on a dataset that is provided by the Tianchi Big Data Competition. The dataset includes the shopping behavior in April and May and that in June. The following table describes the fields in the dataset.

Field	Meaning	Data type	Description
user_id	User ID	STRING	The ID of the user.
item_id	Item ID	STRING	The ID of the item.
active_type	Shopping behavior	STRING	0: click 1: purchase 2: add to favorites 3: add to shopping cart
active_month	Active month	STRING	The month in which the shopping behavior was performed.

The following figure shows the sample data that is used in the experiment.

Procedure

1. Go to the Machine Learning Studio console.
   1. Log on to the PAI console.
   2. In the left-side navigation pane, choose Model Training > Studio-Modeling Visualization.
   3. On the PAI Visualization Modeling page, find the project in which you want to create an experiment and click Machine Learning in the Operation column.
2. Create an experiment.
   1. In the left-side navigation pane, click Home.
   2. In the Templates section, click Create below Recommendation Based on Object Characteristics.
   3. In the New Experiment dialog box, set the experiment parameters. You can use the default values of the parameters.

      Parameter	Description
      Name	The name of the experiment. Default value: Recommendation Based on Object Characteristics. The name must be 1 to 32 characters in length. Enter a name that meets this requirement, for example, RecommendBasedonCharacteristics.
      Project	The project in which you want to create the experiment. You cannot change the value of this parameter.
      Description	The description of the experiment. Default value: Recommendation based on object characteristics.
      Save To	The directory for storing the experiment. Default value: My Experiments.

   4. Click OK.
   5. Wait about 10 seconds. Then, click Experiments in the left-side navigation pane.
   6. Click RecommendBasedonCharacteristics_XX under My Experiments. The canvas of the experiment appears.
      My Experiments is the directory for storing the experiment that you created and RecommendBasedonCharacteristics_XX is the name of the experiment. In the experiment name, _XX is the ID that the system automatically creates for the experiment.

      Area No.	Description
      1	The components in this area perform feature engineering. The raw data includes only four fields. Feature engineering is performed to expand the dimensions of the raw data. The features in this experiment include the features of the recommendation targets and objects. The recommendation targets are the users to whom items are recommended. The dimensions that are expanded for each user include the number of purchases by the user, the number of clicks by the user, and the click-to-purchase ratio of the user. The click-to-purchase ratio is calculated by dividing the number of clicks by the purchase rate. The ratio describes the purchase intention of the user. The recommendation objects are the items that are recommended to users. The dimensions that are expanded for each item include the number of purchases of the item, the number of clicks on the item, and the purchase-to-click ratio of the item. The purchase-to-click ratio is calculated by dividing the number of purchases by the click-through rate. After feature engineering, the dataset is expanded from 4 fields to 10 fields, as shown in the following figure.
      2	The components in this area train a model. In this experiment, a logistic regression algorithm is used to train a model. You can use the AutoML engine that is preset in Machine Learning Studio to automatically adjust the parameters of the logistic regression component. This way, an optimal model can be obtained.
      3	The components in this area evaluate the quality of the model. The reserved data that is not used to train the model is used to evaluate the quality of the model. Experiments on recommendation are binary classification experiments. In such an experiment, you can use a confusion matrix and a binary classification evaluation component to evaluate the quality of the model.

3. Use the AutoML engine to adjust the parameters of the logistic regression component.
   1. In the top toolbar of the canvas, choose AutoML > Auto Parameter Tuning.
   2. In the Auto Parameter Tuning dialog box, keep Logistic Regression for Binary Classification-1 selected in the Select Algorithm step and click Next.
   3. In the Configure Parameter Tuning step, set the parameters for parameter tuning, as described in the following table. Then, click Next.

      Parameter		Description
      Data Splitting Ratio		The split ratio of data. Select 0.7 from the drop-down list.
      Parameter Tuning Method		The parameter tuning method. Select EVOLUTIONARY_OPTIMIZER from the drop-down list.
      Exploration Samples		The number of exploration samples. Select 5 from the drop-down list.
      Explorations		The number of explorations. Select 2 from the drop-down list.
      Convergence Coefficient		The convergence coefficient. Select 0.5 from the drop-down list.
      Logistic regression for binary classification	Regularization Type	The regularization type. Select None from the drop-down list in the Custom Range column.
      	Regularization Coefficient	The regularization coefficient. Specify the range 0.1-2 in the Custom Range column.
      	Minimum Convergence Deviance	The minimum convergence deviance. Specify the range 0.00000001-0.00001 in the Custom Range column.
      	Maximum Iterations	The maximum number of iterations. Specify the range 50-500 in the Custom Range column.

   4. In the Configure Model Output step, set the parameters for model output and click Next.

      Parameter	Description
      Generated Models	The number of models to be generated. The system automatically calculates the number based on the values of the parameters in the Configure Parameter Tuning step. You do not need to set this parameter.
      Algorithm Type	The type of the algorithm to be used. Select Binary Classification Evaluation from the drop-down list.
      Evaluation Criteria	The criterion for evaluation. Select AUC from the drop-down list.
      Saved Models	The number of models to be saved. Select 5 from the drop-down list.
      Pass Down Model	Specifies whether to pass down the optimal model to subsequent components. Turn on the Pass Down Model switch.

   5. In the Auto Parameter Tuning message, click OK.
   6. In the top toolbar of the canvas, click Run.
   7. After the Logistic Regression for Binary Classification-1 component is run, right-click this component and select Parameter Tuning Details. In the AutoML-Parameter Tuning Details dialog box, view the parameter tuning details.
4. View the model evaluation report.
   1. After the experiment is run, right-click Binary Classification Evaluation-1 on the canvas and select View Evaluation Report.
   2. In the Evaluation Report dialog box, click the Charts tab and view the receiver operating characteristic (ROC) curve.
      The blue area represents the area under curve (AUC) value. A larger blue area indicates higher model quality.
   3. Right-click Confusion Matrix-1 on the canvas and select View Evaluation Report.
   4. In the Confusion Matrix dialog box, view the evaluation report.
5. Deploy the model as a RESTful API.
   If the model meets your expectation, click Deploy in the top toolbar of the canvas to deploy the model as a RESTful API. For more information, see Deploy a model.