Item item filtering vs collaborative filtering



i designed music recommendation system. 1st approach was to use item item matrix(co-occurence matrix) . 2nd approach was to make song vs user matrix(with listen count as values) and calculate both song and user features (just like we do in movie rating) using collaborative filtering linear regression.

But the results for both of them on same user are pretty different.So, is it normal or am i doing something wrong?

i checked my 2nd approach and it fitted well my training examples(means it predict listen count which we already have pretty closely)

So,any suggestions???


Hi @ffffg,

Item item collaborative filtering is a part of collaborative filtering. We have two types of collaborative filtering:

  1. User user collaborative filtering

  2. Item item collaborative filtering

In user user collaborative filtering, we find similarity between users and based on the items liked by similar users, make recommendations. In item item collaborative filtering, we find similarity between items and based on the items liked by a user, recommends similar items to the user. To learn about these filtering techniques in detail, refer this article:

The results from these methods can be different as the technique used are different.