Basic of ‘Recommender System’ ML :

Recommender System

A recommender system predicts users interest and recommends product that the user may be interested in .

Gathers data from :

  • Explicit ratings that users provides.
  • Implicit search engine queries and purchased histories.
  • Other source of knowledge about the users/ item.

Recommender system is an information-filtering technique that provides users with recommendations for which they might be interested in.

Recommender system act as a solution for your day to day choices.

  • Which websites will you find interesting ?
  • Which degree and university are best for your future ?
  • -Which is the investment for supporting the education of your children ?

Paradigms of Recommender System :

Paradigms of Recommender System
  • Reduce the information overload by estimating relevance.
  • Personalised recommendation : Recommends the most relevant item based on user profile and contextual parameters.
  • Collaboration filtering recommendation : Recommends what’s popular among your peers.
  • Content based recommendation : Displays products similar to the products you have like before.
  • Knowledge – based recommender system : Recommends the products based on user’s requirement.

Collaboration Filtering :

Collaboration filtering system

Collaboration filtering system : Collaboration filtering system makes recommendations based on historic preferences of the users.

User – based Nearest Neighbour : User – based Nearest Neighbour recommends items by finding user to active user.

Measuring User Similarity : Pearson Correlations

Measuring User Similarity : Pearson Correlations
  • Pearson Correlations measure of how strong a relationship is between two variables.
  • Degree of linearity can be determined by using Pearson Correlation.
  • It determines linear component of association between two continuous variables.
  • It has no assumptions about linearity.
  • Pearson’s correlation is not used to determine the strength.

To read “All About Supervised Learning” :



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Kartikeya Mishra

Kartikeya Mishra


All about new technology in fun and easy way so that you can be confident in it and make your own piece of work using this knowledge !