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I work now with the Graphlab. After installing of this program, I could run the algorithms of Collaborative filtering. Now I try to work with Graphlab in the Python. I have found already this brilliant toolkits. My question is, does anybody know about Collaborative Filtering in the python implementation? I couldn't find this tool & it seems me it doesn't implemented for Python...
Thank you in advance.
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What Is Collaborative Filtering? Collaborative filtering is a technique that can filter out items that a user might like on the basis of reactions by similar users. It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user.
Collaborative filtering systems have many forms, but many common systems can be reduced to two steps: Look for users who share the same rating patterns with the active user (the user whom the prediction is for). Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user.
Amazon is known for its use of collaborative filtering, matching products to users based on past purchases. For example, the system can identify all of the products a customer and users with similar behaviors have purchased and/or positively rated.
Collaborative filtering tackles the similarities between the users and items to perform recommendations. Meaning that the algorithm constantly finds the relationships between the users and in-turns does the recommendations. The algorithm learns the embeddings between the users without having to tune the features.
Check out the recommender package in GraphLab Create. It lets you create a collaborative filtering model in just a few lines.
import graphlab
sf = graphlab.SFrame.read_csv('my_data.csv')
m = graphlab.recommender.create(data)
recs = m.recommend()
You will likely be most interested in the item similarity models, but you should also check out the other options for the method argument, such as matrix_factorization.
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