Recommendation system(Part-2)

 

Collaborative Filtering

Biggest Drawback

  • Cold Start for item: Items that are not rated by any user, cannot be recommended.
  • Cold Start for user as well as the users who have not consumed/rated can’t be recommended to
  • Fall back to most popular items (impersonalized)

    Solution to new user cold start

    Maximising the probability of a signal (click-bait).

    Item Latency: The time between an item being introduced to the catalogue and the first time it gets

    recommended is called item latency.

    The previous rating formula did not take into consideration whether the user is a harsh rater or not, to take

    all these into account the formula was updated and the new formula is:

    This formula tells how far the user is his/her rating for this item going to be.

    Item Based Collaborative Filtering

    • Looks at the similarity of items

    • Uses cosine similarity between the item i and the item rated by user uk 

    • Looks for items rated by users with similar ratings ti item i.

    Pearson correlation:  It is used to find the linear relation between two items. Looks into only those users that

    have rated item i and j.

    If a user has not rated both the items then that user is not considered.As the number of common users rating

    the items may be small, the similarity score is often damped.

    Rating Prediction Using Matrix Factorisation 

    Matrix factorisation is a class of collaborative filtering algorithms used in recommender systems. Matrix

    factorisation algorithms work by decomposing the user-item interaction matrix into the product of two

    lower dimensionality rectangular matrices.

    Where, U and V are nXk and mXk matrices and k are the latent/hidden factors.
    The U matrix tells how important are the preferences for a particular user. It is the representation of users
    based on their k interests.
    The V matrix tells how well an item satisfies preferences. Representation of movies based on its ability to
    fulfill the user’s preferences.
    The multiplication of U and VT results into a matrix that has all the item ratings.

    Cost Function

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