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You could use clustering with a more basic similarity measure, for example cosine or even simply the proportion of words in common (e.g. Jaccard, overlap coefficient). This should gives you groups of sentences which are "quite similar" against each other, whereas sentences in different clusters are supposed to be very different. This way you would ...


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You write ... the model needs to be evaluated against each product in real-time., which gets me thinking that you use a binary classification (sigmoid in the final layer) architecture with negative sampling for the user/item interactions when training your model. Have you considered using multi-class classification instead? Thus, for the user only predict ...


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In this task you're missing something: you don't have any features to represent a specific visitor. This means that the best movie that your model can predict is the one which is selected the most often by any visitor. As a consequence, the only thing that the model can learn from such a dataset is to associate every possible sequence with the most ...


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Pure hype for neural-everything, supported by parties with an interest in such methods (e.g. hardware vendors and cloud providers). For recommender systems this is partly to blame on badly design experiments in earlier research too (see e.g. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches or On the ...


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Clustering and recommendation are similar tasks, however in recommendation you usually want to recommend several items while clustering usually assigns each sample to only one cluster. Anyway for your problem a clustering or even a classifier might help. If labels are assigned on the basis of a similarity metric (and you have a good guess of what this metric ...


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I understood once that one reasons is because of the loss function. Classical ML (non DL) can handle correctly point wise and pair wise ranking systems. But for listwise there are a lot of problems. One of the advantages that DL methods have over non DL methods is teh loss function. With NN you can have a much more flexible loss function and you can tackle ...


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"Recommender Systems" is a very broad area and can be approached from different optics: latent variable models, graph models, etc. "Deep learning" is an umbrella term for gradient-based optimization of deep differentiable models, and has been used to model all sorts of supervised learning problems, including graphs and latent variable ...


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