I am interested in a framework for learning the similarity of different input representations based on some common context. I have looked into word2vec, SVD and siamese networks, all of which are similar to what I want.

For example, suppose we have some customers we are sending different advertisements to, and I would like to create a system to map offers to customers. I am thinking in the lines of creating a customer representation, and a representation of the offers, and feeding them in parallel to a neural network that has a label of whether they acted on the advertisement or not. The idea is that I should be able to locate the best offer for any customer given these representations.

I have looked into siamese networks and word2vec, both are close to what I want. The problem differs slightly in that for the siamese networks, it tends to be identical parallel networks, which I don't want because my inputs are not equivalent. And for word2vec the vectors tend to be in the same domain, while I want to apply this in a more general setting.

If anyone has any resources on a similar problem statement, I would be very interested in it.


1 Answer 1


The idea is that I should be able to locate the best offer for any customer given these representations.

I think you need a Recommender System. As you want to map the offers to customers based one their representation you can check Content-Based Recommender System.

The method is by taking pattern from a customer history and try to find similarities with the new offers. You can use many techniques for a recommender system, from a simple TF-IDF or Deep Learning for more complex problems.

  • $\begingroup$ Thanks for the article on Deep Lerning methods, it looks very relevant. $\endgroup$ Commented Mar 6, 2019 at 7:59
  • $\begingroup$ @user10283726 Is the idea to use recommender system suitable for your problem? $\endgroup$
    – malioboro
    Commented Mar 6, 2019 at 9:06
  • $\begingroup$ Yes, and I have been reading about it but haven't found exactly what I am looking for yet. But I will read the article you provided, hopefully it will be what I am looking for. $\endgroup$ Commented Mar 6, 2019 at 9:10

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