Recommended algorithms for word similarity

Question

I'm researching viable algorithms/solutions to implement and solve following problem: match users based on their common interests

Example:
U1: skiing, asian culture, meditation, java, crypto
U2: yoga, meditation, management, travel tips USA
U3: programming, travelling, oriental cuisine

I'm considering three dimensions based on word similarity:

• Dictionary synonyms
  • wordnet synsets
• Close semantic similarity (programming > java, travelling > travel tips USA)
  • So far I have considered Levenshtein_distance
• Loose semantic similarity (asian culture >> oriental cuisine, programming >> crypto, asian culture >> yoga, yoga >> meditation)
  • Not sure at all, played with word2vec

Based on these approaches I would like to calculate a relevancy score and match users accordingly.

Thanks for the input!

Answer 1

Levenshtein distance was not very useful for capturing semantic similarity in my experiments.

Wordnet worked well but was slow for large set of words

Word2Vec is good approximation for wordnet but not as comprehensive in capturing all the related words

Also suggest you look at the graph embedding algorithm used in Starspace from Facebook and specially the use case around Facebook page likes and recommendations