I am building a content-based recommendation system for hotel accommodation. I have a hotel name, hotel description and location. I combined hotel name, description and location. Then, applied NLP and converted into vectors using TF-IDF.

I am taking hotel name and location as an input and recommend the hotel based on cosine similarity. I wanted to use word2vec instead of TF-IDF and make a recommendation. Anyone can suggest how to do this?


1 Answer 1


You can find a number of blogs teaching the use of Word2Vec,

If you are building a hotel recommendation system, I think the name of the hotel is not a good feature. Users are less concerned with the name of the hotel than its location or description provided.

  • You can take a small description from the user and the location in which they would like to search for the hotel. Using Doc2Vec, convert the description ( text ) into a fixed-sized vector ( embedding ). Find a hotel whose description is the most similar to the one provided by the user ( maybe using Cosine Similarity or any other measures ).

  • Before this find a hotel that is close to the location provided by the user ( say a hotel which lies in a radius of 2 km from the given location ).

Using these two factors, you can prepare a list of hotels whose location and description match the user's needs.


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