UserKNN

[asuresh1]

in the method predict_similar_first_scores the common_users probably need to be looked at.
Currently with a small test data
[[0. 1. 1.]
[1. 1. 1.]
[1. 0. 0.]
[1. 1. 0.]]
Where 1 indicates seen item and zero indicates not seen. common_users is outputting a null list.
Correct me if i am wrong I thing it should be
common_users = list(set(self.users_id_viewed_item.get(self.items[item], [])).
intersection(neighbors[1:self.k_neighbors]))

common_users = list(set(self.users_id_viewed_item.get(item, [])).
intersection(neighbors[1:self.k_neighbors]))

[arthurfortes]

Hi, Suresh!

You're right. Items in u_list are represented as index.

If you can, pull the request.

Thanks for your attention!

[arthurfortes]

Problem fixed in the new version.

[asuresh1]

Sorry for this, got busy with something else. I was thinking for creating the ensemble, the scores in each of the recommenders need to out a score in the same range.
Suggestion is to normalize the scores from 0 to 1 after sorting so the first item to be recommended by any of the algorithm would have a 1. So as to create an ensemble model the scores for each item might have to a dictionary. Correct me if i am wrong. We can chat over skype or something , my email and skype id is suresh.aswathnarayana@live.com.

[arthurfortes]

Hi, Suresh!

How are you?

I'm very busy this month. I just got back from a conference trip. I'll get organized and I'll get back to you soon to talk. Sorry for the delay.

My skype: fortes-arthur@hotmail.com

Regards.

[arthurfortes]

'Suggestion is to normalize the scores from 0 to 1 after sorting so the first item to be recommended by any of the algorithm would have a 1.' I did this in my ensemble algorithms, which I thought was more logical. Remembering that I make this approach to the ranking of each user isolated.