AI & MACHINE LEARNING PROGRAM • LEVEL 16 — RECOMMENDATION SYSTEMS

Predict a Collaborative Rating with Python

Learn predict a collaborative rating with python with a short, executable Python example.

IntermediateCollaborative filteringWeighted average

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

4.2

COMPLETE PYTHON PROGRAM

Complete Python implementation

similarities=[0.9,0.6]
ratings=[5,3]
prediction=sum(s*r for s,r in zip(similarities,ratings))/sum(similarities)
print(round(prediction,2))

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

4.2

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Initialize the sample values used to predict a collaborative rating.
Process the data step by step using Collaborative filtering and Weighted average.
Display the result for predict a collaborative rating and compare it with the documented sample output.

This example of predict a collaborative rating processes the sample values in a controlled iteration. It demonstrates Collaborative filtering and Weighted average and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(d)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For predict a collaborative rating, keep the data shape and value types consistent with Collaborative filtering.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.