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I would like to build a recommendation system based only in the items metadata.

I have an input vector with some desirable topics that the user want to read about, for example: (self-help, yoga, sports)

On the other hand I have a dataset with books with Title, Description, among other fields.

Up to now I am building this model locally with Python, using clustering to group books by similarity.

However, I would like to build a Recommendation System using Amazon Personalize for this case using only item metadata. I do not want to add other info as ratings for now.

Is it possible? Do you know some example?

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As far as I have researched, Amazon Personalize is not useful for this project.
This is because Amazon Personalize requires User-Item Interaction data to train the model. To use Amazon Personalize, you need a dataset with historical data that contains at least the fields: 

User 
Item 
Date

where the Date field represent the time when the user interacted with the Item.

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