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COMP9065 Recommender Systems

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IntroductionYou are given four files of data, as follows:• ratings.txt: Each line of the file contains one rating. This file contains 100,000 ratings. You should use this file to run experiments.• debug.txt: Small subset of ratings.txt which can be used for debugging.• userterms.txt: Each line contains user id, demographic data (age, gender, occupation) which can be ignored for this assignment, and a simple binary term vector for 19 genres.• movies.txt Each line contains movie id, title, release date, etc and the final19 elements of the line are the term vector for the 19 genres.The 19 genres are: Unknown, Action, Adventure, Animation, Children’s, Comedy, Crime, Documentary, Drama, Fantasy, Film-Noir, Horror, Musical, Mystery, Romance, Sci-Fi, Thriller, War, and W

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Part A:
Most of the internet products we use today use recommender systems extensively.
Youtube, Pinterest, Netflix, Amazon, and long list of other internet based products rely
on recommender systems to filter millions of user contents and make personalized
recommendations to their users.
There are two main types of recommender system: Content-based and
collaborative-filtering type recommender system.
Content-based recommender system
This type of filter does not involve other users other than ourselves. It gives
recommendation to user based on similarity of user and item on item features.
In content-based, there will be less diversity in the recommendations, but good thing is
ti works whether user rates movies or not.

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