The Internet has completely changed how customers interact with products and how businesses interact with customers. Unlike physical stores, it is difficult to know the customers’ expectations, likes, and dislikes in the online space. Hence companies are turning towards recommender systems to know their customers and more.
A recommender system is a machine learning model that will filter all the user’s previous purchases and interests to rate what the user might be interested in. The model’s objective is to suggest products or services the user will like.
Why are recommender systems recommended?
- Customer retention: OTT platforms like Netflix and Hulu are based on monthly subscriptions. And they have to keep convincing the customers to pay for another month. Recommender systems are the best way to ensure customers are engaged in discovering new items. When the customers find relevant recommendations, they continue with the subscription.
- Market analysis: Recommender systems give insight into customer behavior on how they interact with a particular product or service. By analyzing the user rating and the number of users engaging with a product, we can understand which product leads the market and what features contribute to its success.
- Increases revenue: User experience directly translates into revenue. If you offer customers what they like, they will stay with your business. Amazon recommends products to customers by analyzing their search results and purchase history. Hence they have a clear idea of what the user will like and don’t like, which means better customer experience and, in turn, better sales. Recommender systems contribute 35% of overall amazon’s revenue.
How does the recommender system work?
There are two approaches to recommender systems. The first approach works on what users with similar tastes will like. For example, if a user watches Pulp Fiction on Netflix and proceeds to watch Kill Bill, the system will recommend Kill Bill to a user who watches Pulp Fiction.
In the second approach, the system will assign a “likelihood” rating to all the items the user has shown interest in. Then, the system will filter out the items that don’t match the user’s interest. A User/Item matrix is created from this, and the system will fill in the items the user might like. The model will compare the User/Item matrix with that of other users to identify the close neighbors and generate more likely suggestions.
Types of recommender systems
There are a lot of implementation techniques for recommender systems and are broadly categorized into,
- Collaborative filtering system
- Content-based system
- Hybrid recommendation system
Collaborative Filtering System
The idea of a collaborative filtering system is that if users A and B exhibit similar interests in a product, they might also show interest in other products. This system predicts a user’s interest by analyzing the interest of many different users and provides suggestions by filtering data and deriving patterns from multiple data sources.
It is a relatively simple model to implement and provides high coverage.
The biggest drawback of this system is its inefficiency in recommending new products since there would be no user/item interaction for new products, a common problem in computer-based information systems known as the “cold start.”
YouTube recommends videos based on the user’s previous viewership, and Coursera’s recommendations based on the completed course of the user are all based on a collaborative filtering recommending system.
This system generates recommendations based on the user’s profile and preference. The similarity between the items is established based on their attributes like categories, types, colors, etc. If a user has shown interest in an item in the past, a similar item will be suggested. The content-based model requires item-level and user-level data sources to function.
While the collaborative approach derives recommendations by matching the target user with other users, the content-based approach relies solely on the target user’s rating.
This model can function even with minimum rating data. With user-level and item-level data, this model can derive ratings from items with similar attributes that the user has rated.
This model restricts the range of recommendations if the user is looking for a new item category with which they have never interacted.
Amazon’s product suggestions and Spotify’s music recommendations are based on a Content-based recommendation system.
Hybrid Recommendation System
Each recommender model has its own demerits and merits. Hence a single type of system will rarely be enough, especially when the problem requires data from different sources.
Hybrid recommender systems are created by combining different models to give robust and personalized recommendations.
Hybrid recommendation systems can be either based on parallel design or sequential design. In the parallel design, the input is given to multiple models, and the recommendation combines the different outputs.
In the sequential method, the input is passed through each model, and the output of one model serves as an input for the next model until the final output is achieved.
Hybrid models reduce the limitations of using single models by combining different models and are free of cold start problems. They also provide more relevant and personalized recommendations compared to single models.
Hybrid recommendation systems have high computational complexity and need large databases from different sources.
Netflix uses a hybrid recommendation combining collaborative filtering(similar user-based) and content-based(user’s and content’s ratings) recommendation models.
Recommender systems are an essential aspect of running an online business. Talk to our experts if you want to improve the user experience, boost sales, and get customer analytics for your business.