Restaurants Recommendation System

  1. Data Collection
  1. Collaborative Based Filtering.
  2. Content Based Filtering.
  • Collaborative Based Filtering
  • > User-based Collaborative Filtering
  • > Item-based Collaborative Filtering
  • To implement item-based collaborative filtering, K-NN is a perfect choice and also a very good baseline for recommender system development.
  • K-NN does not make any assumptions on the underlying data distribution but it relies on item feature similarity.
  • When k-NN makes an inference about a restaurant, k-NN will calculate the “distance” between the target restaurant and every other restaurant in its dataset, then it ranks its distances and returns the top K nearest neighbor restaurants as the most similar restaurant recommendations.
  • Euclidean distance is unhelpful in high dimensions because all vectors are almost equidistant to the search query vector. Therefore, we will use cosine similarity for the nearest neighbor search.
  • Since we have a huge number of records, by taking any random restaurant, and based on the user reviews and interest we’ll get recommendations.
  • Content Based Filtering
  • For calculating the results we used ‘PyCaret’ library which is a built-in package in python.
  • PyCaret is an open-source, low-code machine learning library in Python that allows us to go from data preparation to model deployment.
  • PyCaret calculates all the required metrics like MSE, MAE, RMSE, R2 score, etc on its own and sorts the models in descending order based on the performance.




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