Background: With the proposal of collaborative filtering algorithm, recommendation
system has become an important approach for users to filter excessive Internet information.
Objective: A class correlation distance collaborative filtering recommendation algorithm is proposed
to solve the problems of category judgment and distance metric in the traditional collaborative filtering
recommendation algorithm, which is using the advantage of the distance between the same samples
and the class related distance.
Method: First, the class correlation distance between the training samples is calculated and stored.
Second, the K nearest neighbor samples are selected, the class correlation distance of training samples
and the difference ratio between the test samples and training samples are calculated respectively.
Finally, according to the difference ratio, we classify the different types of samples.
Results: The experimental result shows that the algorithm combined with user rating preference can
get lower MAE value, and the recommendation effect is better.
Conclusion: With the change of K value, CCDKNN algorithm is obviously better than KNN algorithm
and DWKNN algorithm, and the accuracy performance is more stable. The algorithm improves
the accuracy of similarity and predictability, which has better performance than the traditional