Hybrid (Item k-NN and Decision Tree) Recommendation System in RapidMiner

shannoncates_te

Please help us We wanted to implement a Recommender System using hybrid approach (Item k-NN CF and Decision Tree;using gini index). The result should show the performane of its algorithm (RMSE, MAE, and NMAE).

We already established the recommender system but the result only shows the ranking prediction not its performance result. Btw the dataset we use is from MovieLens. Hope this problem will be solve. Thanks!

lionelderkrikor

Hi @shannoncates_te,

 

How said in a previous topic, I don't think that RMSE, MAE, and NMAE are adapted to measure performance of your recommender system. there are dedicated to the performance of a regression model.

However I found the Performance (Item Recommendation) operator . Maybe it will help you to evaluate your (meta)model.

Applied to your model, it give the following results. I do not know what all the score metrics are : 

Here you can find the process : 

<?xml version="1.0" encoding="UTF-8"?><process version="8.0.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="8.0.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Read CSV" width="90" x="45" y="85">
        <parameter key="csv_file" value="C:\Users\Lionel\Documents\Formations_DataScience\Rapidminer\Tests_Rapidminer\ml-latest-small\ml-latest-small\ratings.csv"/>
        <parameter key="column_separators" value=","/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <parameter key="encoding" value="windows-1252"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="userId.true.integer.attribute"/>
          <parameter key="1" value="movieId.true.integer.attribute"/>
          <parameter key="2" value="rating.true.real.attribute"/>
          <parameter key="3" value="timestamp.true.integer.attribute"/>
        </list>
      </operator>
      <operator activated="true" class="split_data" compatibility="8.0.001" expanded="true" height="103" name="Split Data" width="90" x="112" y="238">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.8"/>
          <parameter key="ratio" value="0.2"/>
        </enumeration>
      </operator>
      <operator activated="true" class="set_role" compatibility="8.0.001" expanded="true" height="82" name="Set Role (2)" width="90" x="447" y="289">
        <parameter key="attribute_name" value="rating"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles">
          <parameter key="userId" value="user identification"/>
          <parameter key="movieId" value="item identification"/>
          <parameter key="timestamp" value="timestamp"/>
        </list>
      </operator>
      <operator activated="true" class="work_on_subset" compatibility="8.0.001" expanded="true" height="82" name="Work on Subset" width="90" x="246" y="85">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="rating"/>
        <parameter key="attributes" value="rating"/>
        <process expanded="true">
          <operator activated="true" class="numerical_to_binominal" compatibility="8.0.001" expanded="true" height="82" name="Numerical to Binominal" width="90" x="112" y="34">
            <parameter key="attribute" value="rating"/>
            <parameter key="max" value="3.0"/>
          </operator>
          <connect from_port="exampleSet" to_op="Numerical to Binominal" to_port="example set input"/>
          <connect from_op="Numerical to Binominal" from_port="example set output" to_port="example set"/>
          <portSpacing port="source_exampleSet" spacing="0"/>
          <portSpacing port="sink_example set" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
          <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="500" y="129">Type your comment</description>
        </process>
      </operator>
      <operator activated="true" class="set_role" compatibility="8.0.001" expanded="true" height="82" name="Set Role" width="90" x="380" y="85">
        <parameter key="attribute_name" value="rating"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles">
          <parameter key="userId" value="regular"/>
          <parameter key="movieId" value="regular"/>
          <parameter key="timestamp" value="regular"/>
          <parameter key="rating" value="label"/>
        </list>
      </operator>
      <operator activated="true" class="concurrency:parallel_decision_tree" compatibility="8.0.001" expanded="true" height="103" name="Decision Tree" width="90" x="514" y="85">
        <parameter key="criterion" value="gini_index"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="8.0.001" expanded="true" height="82" name="Set Role (3)" width="90" x="648" y="136">
        <parameter key="attribute_name" value="rating"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles">
          <parameter key="userId" value="user identification"/>
          <parameter key="movieId" value="item identification"/>
          <parameter key="timestamp" value="timestamp"/>
        </list>
      </operator>
      <operator activated="true" class="irbrecommender:item_k-NN" compatibility="5.1.002" expanded="true" height="82" name="Item k-NN" width="90" x="782" y="136"/>
      <operator activated="true" class="multiply" compatibility="8.0.001" expanded="true" height="82" name="Multiply" width="90" x="648" y="442"/>
      <operator activated="true" class="irbrecommender:performance_item_recommendation" compatibility="5.1.002" expanded="true" height="103" name="Performance" width="90" x="983" y="187"/>
      <connect from_op="Read CSV" from_port="output" to_op="Split Data" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 1" to_op="Work on Subset" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 2" to_op="Set Role (2)" to_port="example set input"/>
      <connect from_op="Set Role (2)" from_port="example set output" to_op="Multiply" to_port="input"/>
      <connect from_op="Work on Subset" from_port="example set" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Decision Tree" to_port="training set"/>
      <connect from_op="Decision Tree" from_port="model" to_port="result 1"/>
      <connect from_op="Decision Tree" from_port="exampleSet" to_op="Set Role (3)" to_port="example set input"/>
      <connect from_op="Set Role (3)" from_port="example set output" to_op="Item k-NN" to_port="example set"/>
      <connect from_op="Item k-NN" from_port="example set" to_op="Performance" to_port="train set"/>
      <connect from_op="Item k-NN" from_port="Model" to_op="Performance" to_port="Model"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Performance" to_port="test set"/>
      <connect from_op="Performance" from_port="performance" to_port="result 2"/>
      <connect from_op="Performance" from_port="evaluation measures" to_port="result 3"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

 

 

Regards,

 

Lionel

 

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