def discover_similar_users(character, language_model): # Simulating looking similar pages predicated on vocabulary concept similar_users = ['Emma', 'Liam', 'Sophia'] go back similar_usersdef boost_match_probability(profile, similar_users): to own user when you look at the similar_users: print(f" keeps an elevated chance of coordinating which have ")
Three Static Methods
- train_language_model: This technique requires the menu of talks given that enter in and you will trains a code model playing with Word2Vec. They breaks for every single talk toward individual words and creates a list regarding phrases. The newest minute_count=step 1 factor means also conditions having low frequency are thought from the design. The newest educated model try came back.
- find_similar_users: This method takes a good customer’s character in addition to taught words model due to the fact input. Contained in this analogy, we simulate finding comparable users according to code design. It output a summary of comparable user labels.
- boost_match_probability: This method requires a user’s character together with directory of equivalent pages as input. They iterates over the equivalent pages and prints a contact indicating that the user has a greater risk of matching with every similar affiliate.
Perform Personalised Character
# Carry out a customized reputation character =
# Learn the language sort of associate discussions words_design = TinderAI.train_language_model(conversations) I phone call the fresh instruct_language_design method of the brand new TinderAI class to research the text layout of user conversations. It efficiency a trained words model.
# Look for users with the same language styles comparable_users = TinderAI.find_similar_users(reputation, language_model) I call brand new come across_similar_pages type the brand new TinderAI classification to acquire pages with similar vocabulary appearance. It will take the owner’s profile plus the educated vocabulary model as type in and you can yields a summary of equivalent member names. Read more
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