I made a simple recommendation function.

Recommendation function Reasons to worry

――I often saw recommendations in the online shop. image.png

――I saw a recommended friend on SNS

→ Recommendations have been used recently → It's interesting, so I want to find out


Related articles


First make a movie recommendation

--A function that recommends movies that look interesting to users


Algorithm (1)

image.png


Algorithm (2)

image.png


Algorithm (3) -Scoring

--Highly related → High score --High score → Many recommendations


I wrote scoring in Rails.

    def recommend_movies # recommend movies to a user
      # find all other users, equivalent to .where(‘id != ?’, self.id)
      other_users = self.class.all.where.not(id: self.id)
      # instantiate a new hash, set default value for any keys to 0
      recommended = Hash.new(0)
      # for each user of all other users
      other_users.each do |user|
        # find the movies this user and another user both liked
        common_movies = user.movies & self.movies
        # calculate the weight (recommendation rating)
        weight = common_movies.size.to_f / user.movies.size
        # add the extra movies the other user liked
        common_movie_ids = common_movies.pluck(:id)
        user.movies.each do |movie|
          next if common_movie_ids.include? movie.id
          # put the movie along with the cumulative weight into hash
          recommended[movie] += weight
        end
      end
      # sort by weight in descending order
      sorted_recommended = recommended.sort_by { |key, value| value }.reverse
    end

Display in Rails

https://github.com/phamthanhthuongbk/recommendation-rails

image.png


First, make a friend's recommendation.

--Make the same as the movie algorithm.


Result is

https://github.com/phamthanhthuongbk/recommendation-rails

image.png


Impressions

――The algorithm is easy to understand, and I saw the effect. ――I have an image of the recommended movement. ――For scoring, you have to look at all the DBs, so how about a big system?

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