Machine learning-based recommendation systems are overtaking traditional rules-based systems, and companies like Amazon, Netflix, and Spotify are leveraging these technologies to provide personalised experiences to their users.
Cencosud, the third largest listed retail company in Latin America, has started using a “machine learning (ML) based recommendation system”, after which the brand witnessed a 600% increase in click-through rates and a nearly 26% increase in average order value compared with their prior non-ML driven approach.
The retail company chose Amazon Personalise – a fully managed ML service from Amazon that uses a brand’s data to generate item recommendations for their users, thereby optimising the online shopping experience and boosting user engagement.
Behind Amazon Personalise
Amazon Personalise allows businesses to create custom recommendations for their applications running on Amazon Web Services (AWS) infrastructure. The service generates personalised recommendations with minimal code needed by making an application program interface (API) call.
It uses algorithms to analyse customer behaviour and buying patterns and then makes tailored recommendations based on that analysis. To use Amazon Personalise, businesses need to take the following steps:
- First, the data must be formatted and input into the service. This can include inventory and user demographic information, which can be retrieved from an Amazon S3 bucket or streamed through an Amazon Personalise API. The data can include various activities, like clicks, page views, and purchases.
- The next step is to provide recommendation data for the service. This includes relevant contextual information and a list of items that can be recommended, such as articles, products, or media.
- Amazon Personalise then processes and examines the data to identify key insights. Based on these insights, an algorithm is selected to train and optimise a personalised solution unique to the business’s data.
- Once the solution is trained, it is deployed and implemented into applications through an API call. These integrations can include websites, mobile apps, social media platforms, content management systems (CMS), and email marketing software.
This allows businesses to provide customers with a more personalised and relevant experience.
More tools for developers
Using pre-built tools and frameworks can save time and effort in development, reducing costs and providing a competitive advantage for businesses that offer personalised experiences. Some of the tools include:
- Hugging Face – An open-source library that provides state-of-the-art natural language processing (NLP) models to build chatbots, virtual assistants, and other applications that require personalised language understanding.
- Microsoft Azure Personalizer – A cloud-based service that provides personalised recommendations for products, content, and other items.
- Google Cloud’s Recommendations AI platform – A managed service using ML to provide personalised product recommendations for customers. It can handle various recommendation types, including search results, product recommendations, and content recommendations.
- IBM Watson Studio – Allows developers to build and deploy AI models using various tools and frameworks. It offers a variety of machine learning and deep learning tools, including recommendation engines.
- Salesforce Einstein – A suite of AI-powered tools that can help businesses deliver personalised customer experiences. It includes a recommendation engine that can provide personalised product recommendations and tools for personalised marketing campaigns and customer interactions.
Developers can use these tools and follow certain steps to ensure a reliable recommendation system.
- Collect and preprocess data: Start by collecting relevant data about users and products and preprocessing it to ensure it is clean and ready for analysis. This data could include user behaviour, transactional, demographic, and more.
- Choose an ML algorithm: Choose an appropriate machine learning algorithm to analyse the collected data and make accurate recommendations. Some popular algorithms for recommendation systems include collaborative filtering, content-based filtering, matrix factorisation, and deep learning techniques such as neural networks.
- Train the algorithm: Train the algorithm using historical data to learn from patterns and make accurate recommendations.
- Evaluate and improve the algorithm: Evaluate its performance by measuring how accurately it predicts user preferences, then improve it by tweaking the model parameters, using more data, or experimenting with different algorithms.
With such personalisation, brands can increase revenue as users are more likely to purchase when presented with recommendations that match their preferences.
The necessity to move beyond
The rules-based recommendation systems have been successful in the past, but they struggle to handle the complex and dynamic nature of big data.
Traditional recommender systems assume that user preferences remain the same over time and give equal importance to all of a user’s history records. However, user preferences can change due to their evolving tastes, personal experiences, or the influence of popular trends. This is a common occurrence in Big Data streams known as concept drift.
Furthermore, rules-based systems can be inflexible and limited in their ability to handle complex data and relationships between users and items. This can make identifying patterns and correlations in the data challenging, leading to inaccurate or incomplete recommendations. On the other hand, machine learning algorithms can analyse vast amounts of data, identify patterns and correlations, and make more accurate predictions.
Another limitation of rules-based systems is their limited personalisation. Predefined rules may not capture each user’s preferences, resulting in recommendations that do not reflect the user’s unique interests and needs. Machine learning algorithms can provide more personalised recommendations by analysing data on user behaviour and preferences and making predictions based on that data.
Amazon uses a combination of collaborative filtering, content-based filtering, and deep learning techniques for its recommendation system. Besides Amazon, several top players are making use of new-age recommendation systems.
- Netflix’s recommendation system uses collaborative filtering, matrix factorisation, and deep learning techniques to recommend personalised content to its users. It considers factors such as viewing history, ratings, and genre preferences to make recommendations.
- Spotify’s recommendation system uses a combination of collaborative filtering, natural language processing, and deep learning techniques to recommend personalised music to its users. It considers user listening history, playlists, and social interactions.
- LinkedIn’s recommendation system uses collaborative filtering and natural language processing techniques. It considers factors such as job history, industry, and skillset to make relevant for job postings, networking, and content recommendations.
- Google uses ML techniques to recommend content across various products such as YouTube, Google Play, and Google News. It considers user history, search queries, and browsing behaviour to make relevant recommendations.
The necessity to move towards AI-powered systems is peaking. This is due to their ability to analyse vast amounts of data and learn from user interactions to continually improve the accuracy and relevance of their recommendations, ultimately leading to increased user engagement and satisfaction.