Building Recommendation Engines by Gorakala Suresh Kumar

Author:Gorakala, Suresh Kumar
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2016-12-28T16:00:00+00:00

User-based collaborative filtering with the k-nearest neighbors

If we observe the RMSE values in the above model, we can see that the error is a bit higher. The reason may be that we have chosen all the users' rating information while making the predictions. Instead of considering all the users, let's consider only the top-N similar users' ratings information and then make the predictions. This may result in improving the model accuracy by eliminating some biases in the data.

To explain in a more elaborate way; in the previous code we predicted the ratings of the users by taking the weighted sum of the ratings of all users, instead we first chose the top-N similar users for each user and then the ratings were calculated by considering the weighted sum of the ratings of these top-N users.

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