What should I eat today? — the eternal question, one that has plagued humanity all around the world, don’t tell us you’ve never faced it!
We live in an age of unprecedented choice, where your next meal could be anything from a five-course gourmet experience to an easy street food like Pad Kra Pao. Yet, despite the abundance of options, we still find ourselves stuck in a daily food decision paradox, asking the same question over and over again.
Imagine this: You walk into a restaurant, hungry and excited, but as soon as you open the menu, you’re overwhelmed. There are so many options, each dish sounding better than the last. You want to choose the best meal, but you don’t have enough information to make a confident decision, or maybe there’s just too much information to process. Should you go with the chef’s special, the dish with the most exotic ingredients, or maybe just the one with the most stars next to it?
In moments like these, what do we often do? We ask for a recommendation. “What do most people order here?” you might ask the waiter, hoping for some guidance. And more often than not, they’ll suggest the restaurant’s most popular dish, a safe bet, but one that’s based on what’s generally liked, not necessarily what you might enjoy.
This is where recommendation systems come in. The intelligent algorithms help us navigate the sea of possibilities, guiding us toward choices that align with our tastes, preferences, and needs. Whether you’re deciding on dinner, a movie, or your next purchase, recommendation systems are quietly working behind the scenes to make your life just a little bit easier.
The popular dish suggested by the restaurant is an example of an unpersonalized recommendation, where the suggestion is based on popularity rather than your individual preferences. But what if the recommendation could be tailored just for you, considering your tastes, dietary restrictions, and even your current mood? Personalized recommendation systems take it from here, transforming the decision-making process to suit your unique preferences.
Welcome to the first episode of our Advanced Recommendation System Series. In this article, we’ll explore the concept of personalized recommenders, providing a foundation for the topics to follow. Next, in Sertis Recommends: Our Solutions for Personalized Recommendation Systems, we’ll discuss how our tailored solutions can optimize your business’s recommendation systems. Finally, in Modern Recommenders: A Technical Journey Through Advanced Systems, we’ll review the cutting-edge recommendation system approaches we’ve selected. We’ll outline how these advanced systems work, their effectiveness, and their impact on enhancing personalized experiences in the digital landscape.
What is Personalized Recommendation Systems?
Personalized recommendation systems leverage AI to analyze a user’s historical activities, such as past purchases, viewed items, or search history, to predict what they might enjoy next. By examining this data, the AI crafts recommendations that are finely tuned to each individual’s preferences.
There are various ways to categorize recommendation systems, but in this article, we’ll focus on two primary approaches: Non-Sequential Recommenders and Sequential Recommenders.
Non-sequential recommenders
Non-sequential recommenders make predictions based on a user’s overall interaction history without considering the order of those interactions. For example, a movie recommendation system might suggest films based on the genres you’ve liked, regardless of the sequence in which you watched them.
Traditional approaches in non-sequential recommenders include matrix factorization, user or item-based collaborative filtering, and content-based filtering. While these classical methods are effective, they can be computationally expensive, especially with large datasets, leading to slower performance and scalability issues. Consequently, more recent research has shifted towards deep learning approaches, resulting in newer, more effective methods such as Variational Autoencoder for Recommendations (RecVAE) and Neural Collaborative Filtering (NCF).
Sequential recommenders
Sequential recommenders make predictions by taking into account the order of a user’s interactions, recognizing that the sequence of actions can provide valuable context. For instance, an e-commerce platform might recommend a follow-up product based on the specific sequence of items you’ve recently browsed or purchased.
Traditional approaches in sequential recommenders include methods like Markov Chains and Recurrent Neural Networks (RNNs). While these models capture sequential patterns, they can be limited by their complexity and inefficiencies, especially with longer sequences.
As a result, recent advancements have focused on more sophisticated deep learning approaches, leading to the development of models like Self-Attentive Sequential Recommendation (SASRec) and Sequential Recommendation with Bidirectional Encoder Representations from Transformer (BERT4Rec). These newer methods utilize self-attention and transformer architectures to more effectively capture the nuances in user behavior, providing highly personalized and context-aware recommendations.
Business Use Cases for Personalized Recommendation Systems
Personalized recommendation systems have become a cornerstone for businesses across various industries, enabling them to deliver tailored experiences that resonate with individual customers. By leveraging the power of AI, these systems not only enhance customer satisfaction but also drive business growth through increased engagement and sales. Below, we’ll explore some key business use cases.
Retail Industry
By analyzing a customer’s browsing history, purchase behavior, and even real-time interactions, the recommendation system can suggest products that align with the customer’s preferences, boosting the likelihood of a purchase. For example, an online clothing store might recommend items that complement a customer’s recent purchases, such as suggesting accessories or matching outfits. This approach not only increases the average order value but also enhances the overall shopping experience by making it more relevant and personalized.
Finance
In the finance sector, personalized recommendation systems are used to offer tailored financial products and services. By analyzing a customer’s financial history, spending patterns, and investment goals, banks and financial institutions can recommend personalized investment opportunities, savings plans, or credit products. For instance, a customer who frequently saves a portion of their income might be recommended high-interest savings accounts or long-term investment plans that align with their financial goals, helping them make informed decisions while also increasing customer loyalty.
E-commerce Platforms
E-commerce platforms like Amazon utilize personalized recommendation systems to create a seamless shopping experience. These systems analyze customer data to suggest products based on previous purchases, search history, and browsing patterns. For instance, a customer who frequently purchases electronics might receive recommendations for the latest gadgets or accessories, tailored to their specific interests. This not only helps in cross-selling and upselling but also enhances the user’s experience by making the platform more intuitive and responsive to their needs.
Travel and Hospitality
In the travel and hospitality industry, personalized recommendation systems help companies offer customized travel experiences. By analyzing past travel history, preferences, and even current browsing behavior, these systems can recommend tailored travel packages, accommodations, and activities. For example, a frequent traveler to beach destinations might receive personalized offers for seaside resorts or activities like snorkeling and scuba diving, enhancing their travel experience while also driving repeat bookings.
Conclusion
As we’ve explored in this article, personalized recommendation systems have become integral to enhancing customer experiences across a wide range of industries. By leveraging advanced AI techniques, these systems can tailor recommendations to individual preferences, driving customer satisfaction and business growth.
At Sertis, we specialize in implementing personalized recommendation systems that are not only highly effective but also built on cutting-edge technology. Our solutions can be tailored to your specific industry — whether retail, finance, e-commerce, or beyond — with the aim of optimizing your business’s recommendation strategies and enhancing recommendation results.
In the next two episodes of this series, we’ll delve deeper into how our tailored solutions can be implemented in your business, and we’ll provide a thorough technical review of the advanced methods we use to power these systems. Stay tuned as we continue our journey through the fascinating world of modern recommenders.
Reference
[1] https://edition.cnn.com/travel/article/bangkok-food-thai-dishes/index.html
[2] https://incentify.substack.com/p/discover-weekly-spotifys-accidental
[4] Falk, Kim. Practical recommender systems. Simon and Schuster, 2019.
Originally published at https://www.sertiscorp.com/sertis-ai-research
