Comments on: build a simple recommender system with matrix factorization https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/ A.I, Data and Software Engineering Tue, 05 Oct 2021 10:20:35 +0000 hourly 1 https://wordpress.org/?v=6.9.4 By: Generate data on the fly - Keras data generator - Petamind https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/#comment-69 Tue, 05 Oct 2021 10:20:35 +0000 https://petaminds.com/?p=2075#comment-69 […] we train our model using the pre-generated dataset, for example, in the recommender system or recurrent neural network. In this article, we will demonstrate using a generator to produce data […]

]]> By: tungnd https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/#comment-34 Tue, 23 Mar 2021 02:25:33 +0000 https://petaminds.com/?p=2075#comment-34 In reply to Premobowei Miriki.

Q1: The reindexing is to make it consistent with the python indexing convention which starts from 0 rather than 1 so that it can avoid possible errors.
Q2: We need to compare a number to a number, i.e. rating value ( 5 stars) to predicted value (4.x something) for each user to each movie. So Dot product generates a number and applies for 2 vectors, while matrix multiplication does not serve that purpose.

]]> By: Premobowei Miriki https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/#comment-33 Mon, 22 Mar 2021 21:38:19 +0000 https://petaminds.com/?p=2075#comment-33 Great article very interesting I do have a few questions though. Firstly why did you have to use the reindex what difference does it make to the results and wouldn’t that affect how it matches up to the actual movies dataset. Also, I wanted to ask why you used the dot product for the matrix factorization rather than multiplication. Thank you.

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