Recommendation System(Part-1)

 A recommender system is a system used to predict ratings of an item given by a user and recommend items

Types of Recommender System:

  1. Content Based Filtering
  2. Collaborative Filtering

Content Based Filtering 

  • Track what the user consumes

  • Recommend items similar to those consumed in past

  • Need item description

  • Narrow vision

  • Good for new items

Collaborative Filtering

  • Track all the users consumption

  • No need for item description

  • Recommend items consumed by users who have a similar consumption pattern to the user of interest.

  • More serendipity

  • Recommend based on the neighbourhood of user

Hybrid Filtering

  • Combines the best of two


Recommendation can be viewed as a rating prediction problem.

Accuracy function is computed using metrics such as:

  • Mean square error MSE: ( (r jk - f (uj - ik))2 ) / n
  • Mean absolute error MAE: ( |r jk - f (uj - ik)| ) / n

We create  item rating matrix (R), the data


Where the rows refer to the users and columns to items, the intersection of each row and column tells the rating given by a particular user to an item, a blank column is due to no rating.

This method doesn’t consider the fact that ratings are contextual, as preferences change when one is with family or friends.

These unobserved ratings results in sparseness in the data. In actuality there is a lot of sparse data as getting the rating for each item by each user is not possible. So filling these values is necessary.

User based Collaborative Filtering

In this all the neighbours of active user are considered and the cosine similarity of the active user is calculated with all the neighbours as

similarity(a,b) =  Ua . Ub |Ua| |Ub|

The rating is calculated as:

Rating = similarity(a,K) * rating(K)  similarity(a,K)

Rather than using weighted rating, adjust ratings based on user rating scale because :

  • The users have different rating scales (some are harsh :) 

  • If only one user has rated the item, then the predicted rating will be the same no matter the similarity.

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