Using a decision tree to predict a relevant location to a user I am trying to create a decision tree to predict new locations a user would like to visit based on previous locations they have liked.
Here is my problem, I have two data sets, 


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*a user data set which stores the tags from locations they have previously liked and how many times these tags have been liked by the user:

*a location dataset for locations and their associated tags: 
Is it possible, using decision trees (or another ML algorithm -please specify) to suggest/predict for a user which locations they will 'like' based on the tags of photos they have previously liked?
I am fairly new to machine learning and I am really struggling to think of a way to train my datasets if this is possible. Does anyone have any ideas?
Thank you in advance.
 A: 1) One super simple suggestion (Vector space model in information retrieval): 


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*Each user is represented as an (row) vector

*Each location is represented as a (row) vector


Use as similarity measure the scalar product (cos-similarity). Suggest the locations with the highest similarity score.
for cos-similarity see, eg https://nlp.stanford.edu/IR-book/html/htmledition/dot-products-1.html
2) If you have labels, i.e. users-location pairs (users that like locations) you can use an architecture similar to a "Siamese neural network" (maybe without weight sharing) for finding the similarity between users and locations.
The idea is to map the users and the locations in another feature space
where the similarity is computed by the neural network. 
Without knowing much more about the data (sparsity, synonyms etc.) it is difficult to say more if that makes sense or not. 
for an overview to Siamese NN, see https://www.quora.com/What-are-Siamese-neural-networks-what-applications-are-they-good-for-and-why
