Hello!
In this video, we'll be covering collaborative filtering.
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.
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.com 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 recommendations.
However, Shilling Attacks are the abuse of this system by rating certain products high
and other products low regardless of personal opinion.
Therefore allowing that product to be recommended more often.
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.
It's easy to imagine that a popular item will get a lot of ratings.
These ratings represent information that is used by the system, which in turn, creates
further recommendations to other users.
Now there are more users that'll use, watch, and purchase that product making it even more
popular.
This creates a cycle where new items are just a shadow behind the popular items resulting
in a lack of diversity.
This is called a 'Long Tail' effect.
Thanks for watching!