A Report On Collaborative Filtering and It's Working. Understanding Collaborative Filtering:
Feb. 18, 2024, 1:11 p.m.
Ever thought about how e-commerce sites recommend products to their customers while they are looking for something exactly like that? Ever wondered how Netflix recommends similar movies based on what we have recently watched or added to our watch list?
Artificial Intelligence technology has advanced to such an extent that the world can be perceived through the lens of this technology.
With various techniques like deep learning, machine learning, and artificial neural networks, artificial intelligence tools and techniques have enabled the internet to become a black hole filled with information and entertainment.
In this respect, it has also enabled the internet to recommend users or items to netizens active on the internet.
A variety of machine learning applications and software use recommender systems that are empowered by machine learning techniques and tools for recommending their users’ similar items or products.
Broadly, there are 2 types of recommendation techniques that are in use as of now. First, content-based filtering requires users to enter data that is then processed to produce desired outputs.
Second, the technique of collaborative filtering implies that computers produce outputs based on a user's past interaction on a platform. Herein, we shall understand this with an example.
Let us suppose that an individual is inclined towards romanticism and likes to watch movies belonging to the romantic genre on his Netflix account. Perhaps whenever he logs in to his account, he will see a separate section that will only display recommended movies based on his past preferences and watch history.
How does it work?
Collaborative Filtering is an important machine learning technique that helps a computer to filter information based on past interactions and data recorded on the user's end.
Simply put, collaborative filtering algorithms produce similar results based on the user's historical data. For instance, it has been established that a user is interested in Pop songs.
Perhaps the collaborative filtering algorithms in music streaming applications will record this interaction of the user and interpret that the user prefers Pop Genre over other genres.
The recommendation system built-in with this technique will display other popular songs of the Pop Genre. This is how a collaborative filtering algorithm works.
By recording past interactions of a user on a particular platform, the technique of collaborative filtering interprets and produces recommendation results with similar traits.
Types of Collaborative Filtering
Broadly, there are 2 types of Collaborative Filtering techniques that can be used by software and applications worldwide. They are as follows: -
1. User-based Collaborative Filtering
As collaborative filtering procures its results from implicit data, it is able to retrieve information that users otherwise might not provide. The first class of collaborative filtering is the user-based approach.
This approach narrows down users with the help of collaborative filtering that has similar behaviors, common contacts, and close demographics, and similar consumer behaviors. Social networking sites incorporate this approach to recommend users to other users based on their patterns of behavior.
Moreover, this approach is also employed for targeted ads and suggested items based on other users who have similar choices and preferences. Among the various approaches of collaborative filtering, user-based collaborative filtering is the first approach that came into existence.
A typical example of this approach is the 'suggested friends' category displayed in Facebook. This category recommends people that users might know based on their virtual contacts and similar preferences.
To suggest new recommendations to a particular user, a group of similar users (nearest neighbors) is created based on the interactions of the reference user. The items that are most popular in this group, but new to the target user, are used for the suggestions.
2. Item-based Collaborative Filtering
A class of collaborative filtering techniques, item-based collaborative filtering refers to the recommendation of items or products using collaborative filtering.
By measuring similarity among products and inferring respective ratings, items are recommended to users based on their historical data and interactive history.
This class of collaborative filtering was invented and first used by Amazon in 1998. Even today, e-commerce sites like Amazon and Flipkart use item-based recommendation systems to recommend similar items or products to users by filtering them with the help of a user's past interactive data. \
In item-based filtering, new recommendations are selected based on the old interactions of the target user. First, all the items that the user has already liked are considered. Then, similar products are computed and clusters are made (nearest neighbors). New items from these clusters are suggested to the user.
Case Studies of Collaborative Filtering
In this segment, we will be looking at various real-world case studies that will help us to understand the role of collaborative filtering in a better manner.
1. FACEBOOK
A social networking site that was launched in the year 2004, Facebook has pioneered the world of social networking that aims to connect people from one corner of the world to another.
Currently led by Mark Zuckerberg, Facebook uses numerous techniques of AI that have advanced the social networking site. However, one of the most striking techniques used by this social media giant is Collaborative Filtering.
Be it target marketing suggested friends, or discovering friends, collaborative filtering is a highly significant technique.
2. AMAZON
An e-commerce website, Amazon is a retail platform that sells various commodities and acts as a middleman by connecting sellers and buys from worldwide.
Launched in 1994, Amazon earlier traded in only books. By using a variety of the best machine learning tools for better performance and enhanced user interaction, Amazon also incorporates the collaborative filtering technique for its recommendation system.
Since an e-commerce platform like Amazon has millions of users surfing through the platform, this technique is of great use to the company and its users. With a colossal technological interface, Amazon offers a user-based approach and an item-based approach for suggested products and similar items.
All in all, the platform's item-based collaborative filtering has proved to be a useful system that has triggered the profit-making capacity of the business.
What's more, this platform opts for item-based collaborative filtering more than a user-based approach in order to produce high-quality ecommendations. At first, collaborative filtering had only one approach - a user-based approach.
However, it was Amazon that developed an item-based approach that began to look at items rather than users.
3. NETFLIX
The third case study is based on one of the most renowned OTT platforms worldwide - Netflix. Known for its humongous entertainment collection and latest OTT content, Netflix was founded in 1997.
With millions of users from around the world, the platform offers various recommendations to its users, thanks to a collaborative filtering movie recommendation system.
"Recommendation algorithms are at the core of the Netflix product. They provide our members with personalized suggestions to reduce the amount of time and frustration to find something great to watch.”
Conclusion
To sum up, collaborative filtering is more than just a recommendation technique. It is about the way the internet understands us and offers us the best it can. To some, it might seem to be intrusive.
However, it is utterly genius for a machine to learn our consumer patterns and recommend items based on who is using them and something that resembles our own choices.
Technology has advanced to such an extent that today, machines are filling in the gaps for us. That said, the future of collaborative filtering and other such revolutionary techniques is brilliant on all fronts.
Author:
076BCT089 (Suraj Niroula)
IOE, Pulchowk Campus