Understanding the magic behind AI-powered personalized recommendation engines

Sometimes godsend, sometimes annoying, product recommendations are a part of our day-to-day digital lives. Let’s take a look at behind-the-scenes of AI powered product recommendation engines, and understand the tools, the steps and the algorithms required to make them work.

Why personalized product recommendations?

The future of marketing is personalized, and it is here to stay. Amazon generates 35% of its revenue from recommendation engines. Netflix generates 80% of its user activity through personalized recommendations. Personalization helps brands increase marketing ROI from 10% to 30%, in addition to AOV (Average Order Value) and conversion rate, among other key metrics. 

How does a personalized recommendation engine really work?

A personalized recommendation engine collects and processes customer, product and interaction data, and runs different AI models on it to generate recommendations. It leverages user profile information, browsing and purchase history, add to cart and wish list history, ratings and reviews by the user, returns, search queries, frequency of purchase etc., in combination with product data such as features, price and category. A good model also factors in time of the day, device, context of the customer’s visit, location, and products they are currently viewing. The effectiveness of the engine depends on the quality and variety of data that is fed into it – The more relevant and well-rounded the data, the more accurate and personalized the recommendations are.

What tools and systems does it require?

It requires a multi-layer architecture and a combination of tools across data collection, storage, AI modeling and deployment:

Data Collection: User, interaction, and product data are collected and stored in a database management system such as MySQL or MongoDB. User behavior data from website and mobile app is gathered through analytics platforms like Adobe or Google analytics.

Data Storage: Collected data is stored in cloud platforms like Azure, AWS, or GCP, which also provide computing power.

Data Processing: Tools like Apache Spark, Flink, Hadoop clean and transform the collected data into a format that can be utilized by the recommendation algorithms.

AI Modeling: Machine learning libraries such as scikit-learn, TensorFlow, PyTorch, Surprise, or LightFM are used to build, test and deploy recommendation models.

Serving recommendations: Server side language like Python and a user interface are used to serve recommendations via website, mobile app, email, etc.

Feedback: User feedback on the recommendations is collected to refine the system over time, through user behaviour analytics data, user feedback data or data from A/B tests.

What are the steps involved in building a recommendation engine?

Broadly, a brand needs to do the following to build and deploy a recommendation engine:

• Collect, clean and process data about users, products and interactions.
• Choose features or attributes to make the recommendations.
• Choose a recommendation algorithm. It could be a collaborative filtering, content-based filtering, hybrid approach or deep learning algorithms.
• Train the selected model feeding the collected data into the model and adjusting its parameters to tweak its performance.
• Evaluate the performance of the recommendation model using appropriate metrics such as F1 score which measures precision (how many predicted positives were actual positives) and recall (how many positives were correctly captured) and accuracy (total number of correct predictions divided by total number of predictions)
• Test and implement the recommendation system on the e-commerce site or app.
• Monitor the performance of the recommendation system and retrain the model with new data, tweak the model's parameters, or switch to a different model to optimize it.

Which recommendation algorithms are typically used?

Collaborative filtering is a popular recommendation algorithm. It works on the idea that if two people tend to agree on one thing, they are likely to agree on others. There are two types of collaborative filtering – user based and item based. In user based collaborative filtering, if users A and B tend to buy similar items, then if user A buys a new item, the same item will be recommended to user B. In item based collaborative filtering, if users tend to buy items X and Y together, then if a user buys X, the system recommends Y to that user.

Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.

Hybrid models combine collaborative and content-based filtering to leverage the strengths of both methods. While this method is more effective, it is also more complex and computationally expensive.

There are also deep learning models like Restricted Boltzmann Machines, Autoencoders, which use complex patterns to generate recommendations. They can be used only if the brand has the resources to implement such models.

Each method has its pros and cons in terms of effectiveness, complexity and requirements.

How do you know if it’s working?

Ultimately, the recommendation engine should drive up key business metrics and have positive ROI. The ROI of the recommendation engine is calculated by comparing the increased revenue or another relevant metric against the costs associated with implementing and maintaining the system. The business metrics include revenue, conversion rate, AOV, customer engagement metrics such as time spent and clickthrough rates, customer satisfaction, retention and loyalty scores.

Go deeper into collaborative filtering machine learning (ML) models

Collaborative filtering uses either model-based ML or memory-based ML or a hybrid approach.

Model-based machine learning involves building a mathematical model to make predictions. The model is trained on existing data and then used to predict outcomes for unseen data. A collaborative filtering model learns patterns in user-item interactions and then uses these patterns to predict future interactions. Matrix Factorization is an example of model based machine learning. This method breaks down a large matrix into a product of several smaller matrices to represent only the most important parts. We can then use these matrices to predict the rating a user might give to a product, by looking up corresponding features of the user and the product in our two matrices and multiplying them together.

Memory-based methods make predictions based on their memory of the training data, without building an explicit model. A common memory-based method is user-user or item-item similarity. For example, to predict a user's interest in a product, we might find other users who have similar product preference patterns and use their preferences for the product to make a prediction for the user in question. K-Nearest Neighbors (KNN) is an example of memory based method. KNN predicts the label of a given data point based on the labels of its nearest neighbors. For a product recommendation, KNN will first find the users that are most similar to the user who needs recommendation. Then, it will look for products that those similar users commonly like and recommend them to this user.

Finally, with all the advances in AI, we will see product recommendations becoming more contextual, relevant, real-time and multi-modal. With the increasing demand for transparency, we may see explainable AI for recommendations. There may also be integration of conversational AI allowing users to interact with recommendation systems through speech, text or visual content. As we navigate the future of AI-powered product recommendations, while we don’t know exactly what the future will entail, yet we can be assured that boundaries will be pushed and new possibilities will unfold, revolutionizing the customer experience.

(Article originally published at: https://www.linkedin.com/pulse/understanding-magic-behind-ai-powered-personalized-engines-neha-verma-u727c )