What is AI Recommendation System? Uses & Challenges

ai recommendation system

Recommendation systems have become the backbone of smooth user experiences. You might have seen “Customers who bought this also bought that”, “Your mix playlist” on different platforms, these are examples of how a platform uses recommendation systems. 

Choices are increasing every day and it is becoming difficult for the customers to sort through them and find something that fits their requirements. The main aim of a recommendation system is to actively engage customers by stimulating demand. They can dynamically populate recommendations of products on a given website or app. Recommendation systems use AI to analyze user preferences and historical data to suggest specific products to the customer. It makes the decision-making process for the customer easier.

Amazon increased its sales by 29% after implementing an AI recommendation System. 

A good customer experience makes a platform more desirable and addictive. For the revenue to grow sustainably the platform has to continually improve the customer journey, make the platform easy to use and recommendation engines play a huge role in making the platform easier to explore. AI Recommendation Systems come in handy when you have to serve different customers according to their own tastes.

What are the benefits of a Recommendation System?

AI recommendation systems make the consumer journey easier and memorable, resulting in revenue for the platform/companies. A platform/company doesn’t need to have its own recommendation systems, they can avail the services from 3rd party service providers who have ready-to-use APIs for the existing data. This has made onboarding the recommendation feature for companies transitioning to digitization easier. 

A customer’s confusion can be sorted out if the platform shows them a different choice with the same specifications. This increases the probability of the customer purchasing/ availing the product/ services from the platform. Integrating recommendation systems can do wonders for companies. 

Working of a Recommendation System

The most important part of a recommendation system is the recommender function. The recommender function actually predicts the rating a user might provide to a product even before the user interacts with the product. The more historical data we have concerning a user, the easier it is for the recommendation system to predict the trend. The recommendation engine intelligently selects which filters and algorithms to apply and helps brands to increase conversions and sales.

There are four steps involved in making an AI recommendation system: data collection, data storage, and data analyses.

  • Data collection

    Data of the user like location, age, website click times, liked products, reviews and ratings are stored independently by a platform. 
  • Data labeling

    For building reliable recommendation systems, we need to ensure that the data collected is properly labelled. The data can be either manually annotated or can be done with the help of techniques like Computer Vision or Natural Language Processing. The annotated data helps AI models to identify any data which does not have a label.
  • Data storage

    Data related to a user needs to be stored in the database so it can be used to make recommendations for the user. 
  • Data analysis
    Data analysis can take place in batch, real-time or near real time. Using data analysis algorithms, similar products are suggested to the users depending on their engagement. 
  • Data filtering

    The last step filters down various products on the basis of the user engagement. The kind of algorithms applied(which are explained below) help you filter down the products and show them as recommendations. 

There are three types of recommendation systems data analysis techniques

  1. Collaborative filtering
    It focuses on a single user, the user’s behaviour, preferences and patterns. The ML algorithm here uses the user’s browsing patterns and suggests similar activities. It uses matrix factorization as one of the techniques to find out similar products, services.  It also takes into consideration a customer’s demographics, age to suggest suitable information. The main advantage of this filtering is that it does not need to understand the item itself to do the filtering.
    For instance, if user X likes apples, mangoes, and bananas and user Y likes apples, mangoes, and peaches, then there is a high probability that user X will like peaches too. It is how collaborative filtering finds the recommendations.
  1. Content based filtering

    It works on the principle that if you like an item then you must also like a ‘similar’ item. It makes recommendations using the customer’s past preferences and mapping them with the products (genre, type, colour, etc). It also uses a feature matrix to suggest products. 
    For instance, if a user likes to watch Marvel movies, then the recommendation system might suggest more movies of the same genre or the same actors.
  1. Hybrid model

    It uses both the above approaches to suggest products, services and information to the customer. Hybrid model outperforms both the above filtering models. After an experiment at forbes.com the Click Through Rate increased by 37% after they implemented a Hybrid Recommendation model. 

    For instance, Netflix is a very suitable example for this kind of filtering. It uses collaborative filtering to find other users based on the watching and searching habits of the user. It uses content-based filtering for recommending shows based on the user’s rating of other shows.

Different algorithms used for Data Analysis

Data Analysis is a process of cleaning, transforming data to drive business-related decisions. Companies extract data, process it, and then visualize it to understand the trend. With the trends, they make decisions that benefit the businesses. The inferences are taken from that data and critical decisions are made that would drive the business’s revenue. Many algorithms are used to visualize the trend of data.

Some ML algorithms that are commonly used are Linear Regression, logistic regression, K-means, classification, and regression trees but these algorithms are not very accurate when it comes to personalized predictions.

These algorithms are classic algorithms that follow supervised learning and have been in use for a long time. Linear regression uses the basic quadratic formula to predict values, y = mx + c. Matrix multiplication is the most common mathematics operation that ML algorithms use. 

Regression and Classification Trees divide the data based on questions, like the branching of a tree With the amount of data multiplying every second, the matrix multiplication operations will have to be optimized and the classic algorithms cannot perform well with such huge data sets. Many problems need to be solved in the Machine Learning Space.

Neural Networks are the force behind personalized recommendations. A CNN or Convolution Neural Network is a class of artificial neural networks. When we think of neural networks only matrix multiplication comes to our mind but CNN uses a mathematical technique called convolution. A convolution is an operation on two functions which in turn produces a third function that expresses how the shape of one function is modified by the other. 

Challenges faced by Recommendation systems

Recommendation systems also face a lot of challenges, some of them are listed below:

  1. Cold Start

    This happens when a new user, new item is onboarded to a system. Due to lack of data the system is unable to suggest products, services. Mapping a product which has no user data associated with it like ratings, reviews is virtually impossible. Hence making the job of the recommendation system difficult. 
  2. Data Sparsity

    If a user interacts with a few products, doesn’t provide ratings or reviews it becomes difficult to populate the data matrices for the customer and hence leads to irrelevant recommendations. 
  3. Privacy

    To get more personalised recommendations a user has to pour in a lot of personal information and it evades the privacy of the customer. Data breaches in a network can lead to a lot of information leak and it becomes a potential threat to a user’s privacy. 
  4. Scalability

    As the data increases, the user-item data changes frequently, with the continuous pouring of ratings and reviews it becomes very difficult to scale the system and maintain the accuracy. 

Recommendation Systems help Improve User Experience

Retention of a customer depends on how smooth the customer journey is with the product or service. An AI recommendation system can boost customer reach, improve click-through rates. They speed up the process of searching for useful and relatable products online. It can exponentially improve the user experience and in turn, the customer retention and acquisition rates increase. According to a statement from Amazon’s CEO in 2006, he proclaimed that 36% of their sales happen through cross-sales (recommendation).

The result is a better experience for the customer. This not only keeps the customer happy but also gives your brand an upper hand over your competition.

Use cases

  • Netflix retains revenue

    It is predicted that Netflix retains $1 billion in revenue due to its recommendation system. There are millions of movies, tv series on the platform. A user cannot sit and go through all choices manually, it will be time consuming and the user will get bored of the process and possibly ;eave the platform. To get the customer glued to watching, Netflix recommends similar movies, tv series to the customer, so they don’t have to traverse the whole database to find something they will like. Same is the case with Spotify. One can watch movies on pirated platforms also, but the difference is the customer experience.
  • More scrolling time

    Platforms like instagram, linkedin track the metadata of the posts you have liked in the past and show you similar posts, this results in the user spending more time on their platform. Personalised ad targeting has also become possible due to content filtering. All platforms try to target the users with ads specific to their current needs by tracking their past preferences and data.

How can we help you smooth your customer journey? 

At Queppelin, we have made a recommendation system for music genre classification that can help you make the customer journey on your platform smooth and fulfilling. 

Our recommendation engine uses CNN (Convolutional Neural Network), a Machine Learning algorithm, to provide the user with accurate recommendations. Our ML model can help your platform out with various customized use cases not only for music classification. You don’t need to build your own recommendation system, we can help you out with our services and transition your business that can provide smooth user journeys. 

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