Recommendation systems are disrupting the way users engage with content and make choices. You see recommendation systems in action more often than you might realize. They are typically titled “customers who bought this item also bought” or “users like you also liked” on a webpage.
Users love these systems because they help to provide an optimized experience specific to their needs. Businesses love them because they improve the customer experience, and in turn, their bottom line. It’s a win-win for everyone.
Recommender systems are constantly helping us find what item to buy, what movie to watch, or what article to read.
But have you ever considered how these recommendations are made?
Two popular techniques are often used for recommendation systems: content-based filtering and collaborative filtering.
Content-based filtering works by using stated user preferences or item descriptions to make new item recommendations. This method is often used in streaming or media services.
Collaborative filtering looks at the interactions between users and the set of items they interact with. It then makes new recommendations for items the user has never seen before. This method is frequently used in e-commerce services.
Both of these techniques offer benefits, but for today’s purpose, we will take a deeper dive into collaborative filtering.
What is Collaborative Filtering?
Much like its name states, collaborative filtering leverages collaborative interactions to filter through recommendations.
The technique works in a few basic steps:
- Build a database of user behavior based on a large number of users. (i.e. a collaboration of multiple user interactions).
- Identify patterns to make recommendations based on feedback and user preferences. (i.e. filtering for similar interactions).
- Make automatic predictions on what a user might like based on the behavior history of other users.
Collaborative filtering will generate recommendations for each unique user based on how similar users liked the item. In other words, this method creates a matching system of educated guesses.
Different Types of Collaborative Filtering
There are several different types of collaborative filtering:
- Memory-based collaborative filtering keeps a record of user ratings or user behavior to match the similarity between users or items.
- Model-based collaborative filtering uses data analytics and data mining capabilities to discover patterns and correlations and come up with predictions.
- Hybrid collaborative filtering uses both memory-based and model-based algorithms.
Collaborative Filtering in Action
Companies can track user interactions with their site and create a database of behavioral information. This includes clicks, ratings, purchase information, review time, browsing history, path history and more.
According to Wikipedia, a typical workflow of a collaborative filtering system works like this:
- A user expresses his or her preferences by rating an item or interacting with the item in some other way. This behavior assumes a representation of the user's interest in the corresponding subject matter.
- The system compares this user's ratings against other users and finds which other users have the most "similar" tastes.
- Then, the system recommends items that the similar users have already rated highly, but that hasn’t yet been seen by this particular user.
For example, Sarah is on a sports equipment website. Sarah has purchased a soccer ball and a particular set of cleats.
John has a similar purchase history to Sarah, but John also recently bought a basketball.
Sarah has never searched for anything related to basketball. But, because the system knows John and Sarah have similar interests, the website recommends this basketball to Sarah.
In an ideal world of collaborative filtering success, Sarah buys the basketball and everyone is happy. But this is not always the case.
Shortcomings of Collaborative Filtering
Collaborative filtering uses logic to recommend new products or items to users, but in some cases this isn’t enough. There are many cases where collaborative filtering alone may not be effective.
Articles like this one detail how to create your own recommendation system with collaborative filtering. However, most articles don’t address the challenges or the shortcomings of collaborative filtering in general.
While there are many great benefits, here are some challenges to keep in mind:
Complex: Despite detailed directions, building a collaborative filtering system still requires moderate data science skills and an understanding of big data. If your company does not have a data scientist on your team, this could be a major project to take on.
Scalability: For collaborative filtering to work you must have a large set of user data. You might also need access to a few different data sources. This means you need to have the right infrastructures in place to handle the constant gathering and storing of a growing data set.
Data Assumptions: Collaborative filtering often makes incorrect assumptions that can negatively impact the quality of your recommendations. Collaborative filtering only picks up on positive signals, which means that data is only collected when a user completes a specific action. But only using positive signals makes incorrect assumptions about what content may be relevant to a user.
The Cold Start Problem: Collaborative filtering struggles with tracking for new products or users. How do you make recommendations if there is no historical data? It’s difficult to create personalized recommendations when there isn’t enough data to make inferences on the item or user. Until there is interaction from that new user or engagement with the new item, recommendations cannot be made using collaborative filtering.
While these challenges seem daunting, all hope is not lost. The good news is there are better ways to deliver recommendations optimized for each user.
Leveraging Machine Learning Predictive Analytics for More Relevant Recommendations
At LiftIgniter, we offer a recommendation engine that uses all of the benefits of collaborative filtering without the shortcomings.
Within hours, LiftIgniter starts collecting recommendations saving you valuable time, money and resources.
Saving on time and resources is important, but this isn’t the only advantage to using LiftIgniter. Not only does LiftIgniter require less work from the get go, but it also delivers more relevant results in real time.
Compared to collaborative filtering algorithms, our model integrates more information when making recommendations beyond comparing current users to past users.
We collect many different data points (features) and then use machine learning predictive analytics to identify which features matters. A feature could be a piece of information that we collect, but it could also be an inferred feature from a combination of data points.
Using a model-based machine learning approach, we gain many more "signals" from the behavior history or paths from similar users.
Negative Data Points and Implicit Signals
Negative data points are not leveraged by collaborative filtering. Negative data points are data points that represent options a user didn’t select. For instance, a user is presented with items A, B, and C. They click on B. The positive signal is B, and the negative signal is A and C.
Collaborative filtering only takes into account positive user signals based on the options presented. LiftIgniter takes into account the actions someone doesn’t take as well as the actions they do. Using both the positive and negative signals gives us a deeper insight on user preferences to deliver more relevant recommendations.
LiftIgniter’s use of implicit signals differentiates us further from the traditional collaborative filtering model. Traditional models only take explicit signals, such as ratings, clicks, or purchases, into consideration. LiftIgniter goes further and adds implicit signals, such as browsing patterns, to the equation. The ability to analyze implicit signals is extremely valuable, especially when certain explicit signals are not available.
Why Real-time Matters
By leveraging negative data points and implicit signals, LiftIgniter will provide more relevant recommendations. LiftIgniter’s solution is also more accurate because of real-time analytics.
The LI recommendation engine considers what each user, and all current users, are doing at that moment. LiftIgniter does not rely solely on past behavior assumptions.
Real-time analytics are crucial to improving the customer experience, as you are taking the customer’s current behavior into consideration. But this is easier said than done.
People can change their preferences in an instant. Sometimes they are simply browsing or shopping for someone else. If you don’t adapt the recommendations to the user at speed, you run the risk of losing them.
The most predictive signal of intent comes from what the shopper is doing at that moment. Because collaborative filtering overweights past behavior, the technique inherently cannot react moment-to-moment.
As such, LiftIgniter’s real-time analytics approach is a more powerful way to tackle preferences in real-time.
It’s clear: analyzing historical data and relying on assumptions alone doesn’t cut it. This signal analysis is most effective when paired with a machine-learning-based approach and solid infrastructure setup. It enables the recommendations to constantly learn and act on changing shopper intent in real-time.
LiftIgniter’s real-time capabilities also make it easier to tackle the Cold Start problem. Collaborative filtering requires past user preferences to make recommendations, while LiftIgniter takes cues from the user, and all users, in that specific moment.
A Final Note on Collaborative Filtering
While collaborative filtering offers benefits, is it clear that there are still many challenges to consider.
If you’re looking for a more advanced recommendation solution, LiftIgniter is here to help. After all, the more predictive the recommendations are, the more loyal your customers will be.
With our machine learning and adaptable approach, you can truly create an optimized user experience.