Answers.Body: <blockquote>
    <p>The idea is that I should be able to locate the best offer for any customer given these representations.</p>
  </blockquote>
  
  <p>I think you need a <a href="https://en.wikipedia.org/wiki/Recommender_system" rel="nofollow noreferrer">Recommender System</a>. As you want to map the offers to customers based one their representation you can check <a href="https://en.wikipedia.org/wiki/Recommender_system#Content-based_filtering" rel="nofollow noreferrer">Content-Based Recommender System</a>.</p>
  
  <p>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 <a href="https://towardsdatascience.com/recommender-engine-under-the-hood-7869d5eab072" rel="nofollow noreferrer">TF-IDF</a> or <a href="https://arxiv.org/pdf/1707.07435.pdf" rel="nofollow noreferrer">Deep Learning</a> for more complex problems.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-06T07:59:14.377Z
Answers.Text: Thanks for the article on Deep Lerning methods, it looks very relevant.
Answers.UserDisplayName: user10283726
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-06T09:06:03.593Z
Answers.Text: @user10283726 Is the idea to use recommender system suitable for your problem?
Answers.UserDisplayName: malioboro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-06T09:10:45.247Z
Answers.Text: 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.
Answers.UserDisplayName: user10283726
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-06T03:23:45.240Z
Answers.LastActivityDate: 2019-03-06T03:23:45.240Z
Answers.UserDisplayName: malioboro
Body: <p>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. </p>
  
  <p>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.</p>
  
  <p>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. </p>
  
  <p>If anyone has any resources on a similar problem statement, I would be very interested in it.</p>
  
Tags: <deep-learning><knowledge-representation><word2vec><recommender-system>
Title: Learning similarities between customers and offers representation
UserDisplayName: user10283726
ThreadText: # Learning similarities between customers and offers representation
  
  (Asked by: username_0 on 2019-03-05)
  
  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.
  
  **Tags:** deep-learning, knowledge-representation, word2vec, recommender-system
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-03-06. Score: 0 | Accepted answer
  
  > 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](https://en.wikipedia.org/wiki/Recommender_system). As you want to map the offers to customers based one their representation you can check [Content-Based Recommender System](https://en.wikipedia.org/wiki/Recommender_system#Content-based_filtering).
  
  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](https://towardsdatascience.com/recommender-engine-under-the-hood-7869d5eab072) or [Deep Learning](https://arxiv.org/pdf/1707.07435.pdf) for more complex problems.
  
  > **Comments:**
  > **username_0**: Thanks for the article on Deep Lerning methods, it looks very relevant.  
  > **username_1**: @username_0 Is the idea to use recommender system suitable for your problem?  
  > **username_0**: 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.  
  
  ---
  
  