I am trying to implement simple recommender system and I am trying to understand different approaches to achieve my goal.
My dataset consists of users and items that they bought. I have information about what items user bought and descriptions of these items in form of titles.
At first I though I could use user based collaborative filtering approach but I am stuck at this. I am not quite sure how to calculate similarity for boolean data.
When I have data like this for example
1 2 3 4 A 0 1 0 1 B 0 1 0 1 C 1 0 1 1 D 0 1 0 0 E 0 0 1 1
And I want to recommend items for user E, so how should I calculate similarity in this case? I chosen for example cosine similarity from scikit learn module in python. But I am not quite sure what should be considered as input. From what I read it should be only vectors of items that two users for which similarity is calculated have in common.
So for example if I wanted to compute similarity between user E and C what should be my input? Because if I input only values that they have in common it does not make sense right? Beacuse input will be [1, 1] and [1, 1] and for that similarity is 1.
Then I tried to input the whole vector like this:
from sklearn.metrics.pairwise import cosine_similarity from numpy import array, reshape c = array([1, 0, 1, 1]) e = array([0, 0, 1, 1]) result = cosine_similarity(c.reshape(1, -1), e.reshape(1, -1)) >>> result is 0.81649658
And this approach I think makes more sense but I am not sure if it is acceptable based on what I learned about this type of recommendation.