Similarity functions for user-based collaborative recommendations
11 May 2013
This is from my master thesis where I am writing about recommender systems.
Here are some rankscore results (ten-folds, tested on historical data, read more on evaluation in this great book) after using different similarity functions with a simple user-based collaborative filtering algorithm.
The vectors are binary or, when noted, weighted with the weight 1 / (total downloads) and based on implicit feedback (downloads).
|Euclidian distance (weighted)||0.177710916698|
|Cosine similarity (weighted)||0.212398453763|
I will publish my thesis here in June if you wish to learn more about the dataset, recommender algorithms, weighting techniques and evaluation functions.