Recommender System (SVD) with TensorFlow

Question

I'm trying to create a collaborative filtering algorithm to suggest products to certain users.

I started shortly and started working with TensorFlow (I thought it was sufficiently effective and flexible). I found this code that does what I'm interested in, creates the model and train the user IDs, products, and ratings: https://github.com/songgc/TF-recomm

I launched the code and trained the model.

After training the model I would need to make the predictions, that is, get suggestions for each user so that they can be saved in a DB from which I access with a NODE.js application.

How do I retrieve this list of suggestions for each user when the training is done?

if __name__ == '__main__':
    df_train, df_test=get_data()
    svd(df_train, df_test)
    print("Done!")

Answer 1

You can run

predict_result = sess.run(inter_op, feed_dict={user_batch:users, item_batch:items})

which users means all user ids and items for all item ids, and predict_result is the scores of each user for all items, you can store the predict_result into DB;

Answer 2

You need to modify the prediction part of your code to output top K recommended products. The current code where the prediction is made is :

 embd_user = tf.nn.embedding_lookup(w_user, user_batch, name="embedding_user")
 embd_item = tf.nn.embedding_lookup(w_item, item_batch, name="embedding_item")
 infer = tf.reduce_sum(tf.multiply(embd_user, embd_item), 1)

Here the embed_user is the user embeddings of a particular user and embd_item is for the particular item. So instead of comparing a particular user with a particular item , you need to change it to compare it to all items. The matrix w_item is the embeddings of all items. This can be done by:

 embd_user = tf.nn.embedding_lookup(w_user, user_batch, name="embedding_user")
 # Multiply user embedding of shape: [1 x dim] 
 # with every item embeddings of shape: [item_num, dim], 
 # to produce rank of all items of shape: [item_num]
 predict = tf.matmul(embd_user, w_item, transpose_b=True)

Then you can select the top k index of the maximum in the predicted output.

Answer 3

-Gabriele Picco 我的 tensorflow 建议系统有问题，我可以在哪里与您联系？