Discuss how you would improve upon pure item-based and pure user-based collaborative filtering.
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Pure item based and pure user based collaborative filtering can be improved by :-
1. Memory based Collaborative filtering algorithms :- Memory based algorithms use entire user item database to generate a prediction. These algorithms use statistical techniques to find a set of users called neighbors that could agree with target user. Once a neighborhood is formed, these algorithms predict a recommendation for active user.
2. Model based collaborative filtering algorithms :- Model based collaborative filtering algorithms give recommendation by developing a model of user strings. Algorithms take probabilistic approach and envision a expected value of user prediction based on his ratings on other items.
3. Item based collaborative filtering algorithms :- In this algorithm once similar items are found then prediction is computed by taking a average of target user's ratings on similar items.
Discuss how you would improve upon pure item-based and pure user-based collaborative filtering.
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