Collaborative risk to speed. In comparison, having

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Collaborative Filtering techniques explore the
idea that relationships exists between products and people’s interests. Many
recommendation systems, also called recommender

systems, use Collaborative Filtering to realize these
relationships and to give an accurate

recommendation of a product that the user may
like or enjoy. Collaborative Filtering
bases these relationships on choices that a user
makes when buying, watching, or enjoying
something. Then
makes connections with other users of similar
interests to produce a prediction.

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One popular example of Collaborative Filtering is
Netflix. Everything
on their site is driven by their customer’s selections,
which if made frequently enough, get turned into
recommendations. Netflix orders these
recommendations in such a way that the highest
ranking items are more visible to users, in hopes
of getting them

to select those recommendations as well.


Another popular example is Amazon’s
item recommendation system is based on
what you’ve previously purchased, as well as
the frequency with which you’ve looked at
certain books or other items during previous visits
to their website. The advantages of using
Collaborative Filtering is that users get a broader
exposure to many different products they
might be interested in. This
exposure encourages users towards continual usage
or purchase of their product. Not only does this provide
a better experience for the user,
but it benefits the service provider as
well, with increased potential revenue and
better security for its consumers.


There are some Challenges with Collaborative Filtering. One
of them is Data Sparsity. Having a Large Dataset will
most likely result in a user-item matrix being
large and sparse, which may provide a good
level of accuracy, but also pose a risk to
speed. In
comparison, having
a small dataset would result in faster speeds but
lower accuracy. Another issue to keep in
mind is something called ‘cold start’. This
is where new users do not have a sufficient amount
of ratings to give an accurate recommendation.


Scalability can become issue as well. As
the number of users increases and the amount of
data expands, collaborative filtering
algorithms will
begin to suffer drops in performance, simply
due to the sheer increase in volume. The
term, ‘Synonyms’ refers to the frequency

of items that are similar, but are labeled differently. And
thus treated differently by the recommendation system. An
Example of this would be ‘Backpack’ vs ‘Knapsack’. A
recommendation system may treat these two items
differently because of its labeling even
though, functionally, they’re very similar to
one another.


The term ‘Gray Sheep’ refers to the users that
have opinions that don’t necessarily ‘fit’
or are alike to any specific grouping. These
users do not consistently agree or disagree on
products or items, therefore making recommendations a
non-beneficiary to them.


In a recommendation system, users can give ratings
on products they like or dislike, this is what Collaborative
Filtering uses to determine good


In some recommendation systems, there is a lack of diversity for recommendations. This is because popular items get recommended more often simply because of the fact that more users use and rate them.

Categories: Relationships


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