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I am new to deep learning and I am trying to build a book recommender system using embedding layers. I use one layer for the book and one for the user.

I am having trouble with fitting the model. More specifically, when I try to feed the first layer with the books' ISBN list I get back the " ValueError : could not convert string to float: '087584877X' ".

Note that a book's ISBN ( as '087584877X' for example ) is either a sequence of numbers or a sequence of letters and numbers. The second category seems to be the problematic one.

I dont get why it should convert the string to float. I though that what the network does is to assign each string to a new integer regardless of the structure of the string.

Here is my code for the books embedding layer :

book_input = Input(shape=(1,))

book_embedding = Embedding(input_dim = n_books+1, output_dim = 4)(book_input)
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It looks like you're using Keras, so this answer is going to refer to the Keras embedding layer.

The error message is pretty clear - Keras embedding layers don't accept strings as inputs. The embedding layer requires an array of positive integers and can only be used as the first layer of a model. The embedding layer requires that you encode each word as an integer so you might have to use the Keras tokenizer.

Source: Embedding Layers

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