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C138.txt
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C138.txt
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The rapid growth of data collection has led to a new era of information.
Data is being used to create more efficient systems and this is where
Recommendation Systems come into play. Recommendation Systems are a
type of information filtering system as they improve the quality of search
results and provide items that are more relevant to the search item or
are related to the search history of the user.
They are used to predict the rating or preference that a user would give to
an item. Almost every major tech company has applied them in some
form or the other: Amazon uses it to suggest products to customers,
YouTube uses it to decide which video to play next on autoplay, and
Facebook uses it to recommend pages to like and people to follow.
Moreover, companies like Netflix and Spotify depend highly on the
effectiveness of their recommendation engines for their business and
success.
There are basically three types of recommender systems:
● Demographic Filtering- They offer generalized recommendations to every
user, based on movie popularity and/or genre. The system recommends the
same movies to users with similar demographic features. Since
each user is different, this approach is considered to be
too simple. The basic idea behind this system is that movies that are more
popular and critically acclaimed will have a higher probability of
being liked by the average audience.
Demographic information examples include: age, race, ethnicity, gender,
marital status, income, education, and employment.
((v/(v+m))*R)+((m/(v+m))*C)
Here,
● v - The number of votes for the
movies (or number of
ratings/reviews in case of an
amazon product)
● m - The minimum votes
required to be listed in the
chart
● R - Average rating of the movie
● C - Mean votes across the
whole report
● Content Based Filtering
Content-based filtering is a type of recommender system that attempts to guess what a user
may like based on that user’s activity.
The general idea behind content based filtering is that if a person likes a particular item,
he or she will also like an item that is similar to it.
cast, crew, keywords and genres.
Content-based filtering makes recommendations by using keywords and attributes assigned to
objects in a database (e.g., items in an online marketplace) and matching them to a user
profile. The user profile is created based on data derived from a user’s actions, such as
purchases, ratings (likes and dislikes), downloads, items searched for on a website and/or
placed in a cart, and clicks on product links.
They suggest similar items based on a particular
item. This system uses item metadata, such as genre, director, description,
actors, etc. for movies, to make these recommendations. The general
idea behind these recommender systems is that if a person likes a
particular item, he or she will also like an item that is similar to it.
● Collaborative Filtering-
Collaborative filtering is a technique that can filter out items that a user might like on the
basis of reactions by similar users.
It works by searching a large group of people and finding a smaller set of users with tastes
similar to a particular user. It looks at the items they like and combines them to create a
ranked list of suggestions.
This system matches persons with similar interests and provides recommendations based on
this matching. Collaborative filters do not require item
metadata like its content-based counterparts.
LINK: https://www.kaggle.com/tmdb/tmdb-movie-metadata