The world is transforming from the age of knowledge towards the age of recommendations. Many companies have already successfully adopted recommendation systems, as a way of boosting their bottom line.
But you might be wondering what exactly is collaborative filtering and how it fits into the bigger picture.
To put it simply, collaborative filtering is a recommendation system that creates a prediction based on a user’s previous behaviors.
Recommendation systems have made their way into our day-to-day online surfing and have become unavoidable in any online user’s journey.
That’s why this article will help you understand:
- What is collaborative filtering
- What are the types of recommender systems
- Pros and cons of collaborative filtering
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Why do we need recommender systems?
The main objective of the system is to provide the best user experience. Therefore, companies strive to connect the users with the most relevant things according to their past behavior and get them hooked to their content.
The recommender system suggests which text should be read next, which movie should be watched, and which product should be bought, creating a stickiness factor to any product or service. Its unique algorithms are designed to predict a users’ interest and suggest different products to the users in many different ways and retain that interest till the end.
Needless to say that we see the implementation of this system in our daily lives. Many online sellers implement recommender systems to generate sales through machine learning (ML). Many retail companies generate a high volume of sales by adopting and implementing this system on their websites. The pioneering organizations using recommenders like Netflix and Amazon have introduced their algorithms of recommendation systems to hook their customers.
Before diving into the in-depth mechanics, it is necessary to know that this system removes useless and redundant information. It intelligently filters out all information before showing it to the front users.
To understand the recommender system better, it is a must to know that there are three approaches to it being:
- Content-based filtering
- Collaborative filtering
- Hybrid model
Let’s take a closer look at all three of them to see which one could better fit your product or service.
1. Content-based filtering
Many of the product’s features are required to implement content-based filtering instead of user feedback or interaction. It is a machine learning technique that is used to decide the outcomes based on product similarities.
Content-based filtering algorithms are designed to recommend products based on the accumulated knowledge of users. This technique is all about comparing user interest with product features, so it is essential to provide a significant feature of products in the system. It should be the first priority before designing a system to select the favorite features of each buyer.
These two strategies can be applied in a possible combination. Firstly, a list of features is provided to the user to select the most interesting features.
Secondly, the algorithms keep the record of all the products chosen by the user in the past and make up the customer’s behavioral data. The buyer’s profile rotates around the buyer’s choices, tastes, and preferences and shapes the buyer’s rating. It includes how many times a single buyer clicks on interested products or how many times liked those products in wishlists.
Content-based filtering consists of a resemblance between the items. The proximity and similarity of the product are measured based on the similar content of the item. When we talk about the content, it includes genre, the item category, and so on.
Let’s take the example of recommender systems in movies. Suppose you have four movies in which the user starts off liking only two movies at first. Still, the 3rd movie is similar to the 1st movie in terms of the genre, so the system will automatically suggest the 3rd movie. It is something that is automatically generated by a content-based recommender system based on the similarity of content.
Just imagine the power of content-based recommender systems, and the possibilities are endless. For example, when we have a drama film that the user has not seen or liked before, this genre will be excluded from their profile altogether. Therefore, a user only gets their recommendation of the genre that is already existing in their profile. The system would never suggest any movie out of their genres to present the best user experience.
Let’s get back to the movie example. Imagine that you have only six movie data sets. Let’s for the sake of clarity that the user has seen all these six movies. Then, the genre of all movies is assigned, i.e., Super Hero, adventure, comedy, and sci-fi, with each movie assigned one or a combination of genres.
Now moving further, the user has seen and rated three movies and given a rating of 2 out of 10 to the 1st movie, 10 to the 2nd movie, and 8 out of 10 to the 3rd movie. After these ratings, the recommender system needs to make calculations based on a user profile. Furthermore, the system will recommend the best-suited movie according to the calculations.
The Content-based filtering system does not require any buyer information since the suggestion is only specific to the buyer and makes a scale easier for many buyers. User’s interest is captured by this system and suggests items that few buyers use.
2. Collaborative Filtering
Collaborative filtering needs a set of items that are based on the user’s historical choices. This system does not require a good amount of product features to work. An embedding or feature vector describes each item and User, and it sinks both the items and the users in a similar embedding location. It creates enclosures for items and users on its own.
Other purchaser’s reactions are taken into consideration while suggesting a specific product to the primary user. It keeps track of the behavior of all users before recommending which item is mostly liked by users. It also relates similar users by similarity in preference and behavior towards a similar product when proposing a product to the primary customer.
Two sources are used to record the interaction of a product user. First, through implicit feedback, User likes and dislikes are recorded and noticed by their actions like clicks, listening to music tracks, searches, purchase records, page views, etc.
On the other hand, explicit feedback is when a customer specifies dislikes or likes by rating or reacting against any specific product on a scale of 1 to 5 stars. This is direct feedback from the users to show like and dislike about the product. It includes both positive and negative feedback.
Collaborative Filtering is the most famous application suggestion engine and is based on calculated guesses; the people who liked the product will enjoy the same product in the future. This type of algorithm is also known as a product-based collaborative shift. In this Filtering, users are filtered and associated with each User in place of items. In this system, only users’ behavior is considered. Only their content and profile information is not enough. The User giving a positive rating to products will be associated with other User’s behavior giving a similar rating.
The main idea behind this approach is suggesting new items based on the closeness in the behavior of similar customers.
To understand the concept, let’s discuss another example. If you plan to watch a new movie, you will generally ask your friends and seek their recommendations. This is based on the premise that users trust their friends as they are confident that their friends know their taste in movies. Therefore, we usually follow and watch whatever is recommended by a good friend who has a similar taste.
Thus collaborative filtering focuses on relationships between the item and users; items’ similarity is determined by their rating given by customers who rated both the items.
3. Hybrid Filtering
A hybrid approach is a mixture of collaborative and content-based filtering methods while making suggestions; the film’s context also considers. The user-to-item relation and the user-to-user relation also play a vital role at the time of the recommendation. This framework gives film recommendations as per the user’s knowledge, provides unique recommendations, and solves a problem if the specific buyer ignores relevant data. The user’s profile data is collected from the website, film’s context also considers the user’s watching film and the data of the scores of the movie.
The data consist of aggregating similar calculations. This method is called the hybrid approach, in which both methods are used to produce the results. When this system is compared with other approaches, this system has higher suggestions accuracy. The main reason is the absence of information about the filtering’s domain dependencies and the people’s interest in a content-based system.
When these two approaches work together, you will get more knowledge, leading to better results; it explores the new paths to significant underlying content and collaborative filtering methods with buyer behavior data.
This system has taken to implement both the systems and overcome most of the weaknesses of each system’s algorithms and improves the system’s performance. Classification and cluster techniques are used for getting more excellent recommendations, thus growing accuracy and precision. Our method can be lengthier than other rules to recommend video, song, newsbooks, venue, e-commerce site, tourism, etc.
Interested in finding out more details about recommender systems? Check out our Introduction to Recommender Systems!
Types of Collaborative Filtering
There are two types of the collaborative filtering process:
- Memory-based collaborative filtering
- Model-based collaborative filtering
1. Memory-based Collaborative Filtering
Memory-based CF is one method that calculates the similarity between users or items using the user’s previous data based on ranking. The main objective of this method is to describe the degree of resemblance between users or objects and discover homogenous ratings to suggest the obscured items.
Memory-based CF consist of the following two methods:
a) User-based Collaborative Filtering
In this method, the same user who has similar rankings for homogenous items is known. Then point out the user’s order for the item to which the user is never linked.
Let’s understand this with an example. Consider Harry and Jack are given ranking on few movies:
Harry: Toy Story= 4, Coco= 2, Zootopia=3.
Jack: Toy Story= 4, Coco= 2, Zootopia=?
Now we need to find out the rating of Zootopia, which Jack has never viewed. For this, we need to follow the given steps:
- Identify the target user (according to this example, Jack is the target user)
- Find the same user who has ratings like the target user.
- Explore the interacted items.
- Forecast the ranking of unseen things of the target user.
- If the forecasted rankings are higher than the threshold, then suggest them to the target user.
b) Item-based Collaborative Filtering
In item-based CF, we find the same items that the target user has already viewed.
Jack: finding nemo=4, Moana= 3, Toy Story=4.
- Identify the target user.
- Find the matched items which have the same ratings as items the target user rated.
- Forecast the rankings for the same items.
- If the forecasted rankings are higher than the threshold, then suggest them to the target user.
Though, the Item-based model shows better consequences as compared to the user-based method as the resemblance between items seems to be consistent than the users.
A numerical measure using a similarity matrix is the most common technique. It involves Dot product, Cosine similarity, Pearson similarity, and Euclidean distance.
2. Model-based Collaborative Filtering
Model-based collaborative filtering is not required to remember the based matrix. Instead, the machine models are used to forecast and calculate how a customer gives a rating to each product. These system algorithms are based on machine learning to predict unrated products by customer ratings. These algorithms are further divided into different subsets, i.e., Matrix factorization-based algorithms, deep learning methods, and clustering algorithms.
Normally, the simple cluster algorithm is used like K-Nearest Neighbor to identify the nearest embedding or neighbor consisting of a similar matrix used for a product or a customer embedding. The matrix factorization technique is different from analyzing and exploring the rate of rating matrix in an algebra context and has two main goals. First, the initial ambition is to reduce the rating matrix dimension. This approach’s second ambition is to identify perspective features under the rating matrix, which will provide several recommendations.
In Collaborative Filtering, two more frequent techniques are used. The model-based technique applies a statistics system and machine learning approach for minimizing the rating matrix. Still, the model-based approach does not produce expected results compared to CBF and CF approaches. An extensive database can be handled and infrequent matrices.
Collaborative Filtering is a straightforward interpretation of how these algorithms use crowd data. A large amount of data is gathered from different people and used for creating customized suggestions and preferences of a single user. These methods were developed in the 1990s and 2000s. Social media has brought innovation, and data availability has increased access to information from different sources. The recommended system has begun to use the social network in account in inclusion to similarity.
Examples of Collaborative Filtering
One of the best examples of collaborative filtering can be seen in the area of E-Commerce. When you browse an e-commerce website, you can see that it shows some recommended products to you. Some of the items there are precisely the same as what you were looking for. Now a question may arise about how the website knows what your interests are. It is all just because of collaborative filtering.
In social connection sites, Friends’ suggestion is also very common. For example, on Facebook, a section is displayed known as People you may know; it is a very outstanding feature and shows a list of people to add them as a friend. Based on social connection data, this system educates and guesses the missing edges, like if you are friends with 10th out of 11th densely associated people, it is like you must befriend with 11th. Social connections are built by using the algorithms of collaborative Filtering.
Let’s take one more example. Bob and Alice have the same interest in playing. Bob played it and enjoyed the game a lot. Alice did not play that game yet, but the system has determined that Bob and Alice have the same interest, so the system recommends that game to Alice. Collaborative Filtering can be performed by recommender systems using the same product. The other buyer will like the same item.
One more example of n X m this matrix is made up of the buyer’s rating n refer to buyer and m refer to the item or object. Every element of this matrix is (k,l) how User l rated product k. We are dealing with movie’s show ratings, and every rating should be several between 1 to 5 where follow 1-star rating to 5 stars rating. If the User did not rate a particular movie or the movie l is rated by user k.
One scenario of collaborative filtering is to suggest famous and interesting or popular information judged by the area of people. Then, the stories are shown on the front page of Reddit, which are voted positively by a group of people. As the group of people becomes more diversified, the publicized stories will show a better community interest. Wikipedia is also an application of collaborative Filtering.
Collaborative filtering does not need content extraction and analysis. People will be able to evaluate information accurately as compared to counted functioning. Complex objects or multimedia like music, movies, and images start working well.
Collaborative Filtering vs Content-based Filtering
A number of advantages are provided by Collaborative Filtering over Content-Based filtering. Some of them are:
- For telling the whole story, the item’s content is unnecessary, like movie genre/ type.
- If the information of a product is not available, the product can be rated easily without delay in buying the product.
- Content-focused does not give any adaptability to the user’s preferences and aspects.
- Collaborative filtering relies on other buyer’s ratings to identify the connections between the buyers and provide the best suggestion based on the user’s similarities. As a comparison, the Content-based method just needs to analyse the user’s profile and items.
- Collaborative filtering gives suggestions because most of the unknown buyers have a similar taste to you. Still, in Content-based, you will get the recommendations of items based on product features.
- In contrast to Collaborative filtering, new products are suggested without any specifications by many buyers.
- The cold start is the main problem of Content-based, and it arises when the recommendation system is made up of very few rating records. In this case, content-based filtering is an excellent alternative to this problem.
- Content-based has drawbacks, like the keyword used in the content for representing the item may be not representative. This approach also suffers in making perfect recommendations to the buyers with the very ratings.
Numbers of the drawback of these systems are mentioned below.
- The content-based system is only a design suggestion based on the current interest of the user. Therefore, you can also say that this system is only limited to buyers’ existing desires or interests.
- Since the item representation of the features is hand-setup comparatively, it requires enough domain knowledge; therefore, this model is only with the excellent hand setup features.
- If the content of the product is not good enough to describe the product precisely so the made recommendation will be false at the end
- The content-based approach provides a limited amount of innovation since the item and profile features should be matched. You need to be surprised by an excellent content-based filtering method.
- The system’s correct recommendation cannot be provided unless strong user profile information is put in the system.
There are many pros and cons of every system, whether a content-based filtering system or a collaborative filtering system. As a result, many organizations have adopted a hybrid system to merge the advantages of these systems, as mentioned earlier, and try their best to provide more accessible and more accurate suggestions to their users.
Online buyers and internet users crave personalized experiences. Most users prefer to use recommendations suggested by a different website to save their time because they do not want to waste their precious time searching and getting lost in information. As this trend is evolving, more organizations are using different recommender systems to personalize their business deals.
Implementing a recommender system can be expensive, but surely, you will get the benefits of highly customized content. This creates a unique stickiness to the product offering creating an invisible pull in customers that benefits the organizations.