Driving Growth And Customer Loyalty With Recommendation Systems (Part 1)
The Strategic Imperative of Recommendation Systems
In the present day, there is widespread recognition that numerous digital firms can access, utilize, and monitor copious amounts of data pertaining to consumers. Of note are e-commerce companies, which can gain access to vast quantities of structured and unstructured data derived from consumers' profiles, historical transactional data, regional and seasonal purchasing habits, and even browsing behavior. These data can subsequently be leveraged to model consumer behavior and capture value in a variety of ways - such as driving increased sales and revenue, reducing inventory and transportation costs, and improving customer retention. At the heart of this process lie recommender systems, which play a pivotal role in driving significant engagement and revenue.
As well as the major technology firms, an increasing number of business leaders are prioritizing the development of recommender systems. While the pursuit of growth is undoubtedly a significant motivator, such a perspective may be viewed in a cynical light. Instead, these companies recognize that effective recommender systems can provide tangible benefits to their customers, which in turn can create opportunities to increase revenue. Given the importance of this topic, I have elected to produce a series of articles focused on the development and implementation of recommender systems, with a particular emphasis on generative AI. This first installment is intended to serve as an introduction to the series.
Interaction with Recommenders
The topic of recommender systems often elicits Steve Jobs' famous quote: "a lot of times, people don't know what they want until you show it to them." However, it is worth noting that this statement may be more applicable to companies with the resources to create exceptional products that surpass consumers' imaginations and expectations, such as Apple. For most businesses, the assumption must be made that diligent effort is required to ascertain what customers truly desire. This is where recommendation engines come into play, serving as a critical tool for gaining insight into customer preferences and delivering personalized recommendations.
In formal terms, a recommendation engine or system is an intelligent filtering mechanism designed to present users with content that is tailored to their preferences and deemed to be of potential interest. The true power of these discovery assistants lies in their ability to introduce customers to content that they may not have discovered on their own, in a manner that is both efficient and effective.
Recommendation engines generate value for customers by providing real-time, highly personalized recommendations that can significantly influence their decision-making process and shape their overall user experience on preferred platforms. In turn, these engines also capture value for firms by driving engagement and increasing revenue. Netflix, for instance, reports that approximately 75% of user viewing is attributable to some form of recommendation, underscoring the powerful impact that effective personalization algorithms can have. Implementing a robust recommendation algorithm can also drive customer acquisition, a key objective for many retail companies. Indeed, achieving such a feat would be a significant accomplishment for any retail business.
How it works?
There are two distinct approaches to building recommendation systems: discriminative models and generative recommendation system models. Discriminative models leverage patterns and similarities among users or items to generate personalized product recommendations. In contrast, generative models are designed to generate new items or content based on user preferences and other relevant data. The selection of an appropriate model will depend on the unique needs and constraints of the e-commerce business in question.
The primary objective of discriminative models is to pinpoint patterns and similarities among users or items that can be leveraged to generate personalized product recommendations. To accomplish this, these models analyze user-item interactions and identify commonalities between users or items that can be used to generate recommendations. Collaborative filtering, matrix factorization[1], and deep learning models are all examples of discriminative models used in the development of recommendation systems.
Generative models are specifically designed to produce new items or content based on user preferences and other applicable data. These models can prove particularly valuable when there is a dearth of preference history for certain items or when personalized recommendations are deemed essential. Generative retrieval, generative adversarial networks (GANs), and large language models for generative recommendation are all examples of generative models that can be employed in the development of recommendation systems.
It’s All About Ranking
The ultimate objective of recommender systems is to provide users with a selection of appealing items from which to choose. This is typically achieved by selecting several items and arranging them in order of expected engagement or click rate. Given that recommended items are most often presented in the form of lists or displays, it is essential to have a suitable ranking model that can leverage a diverse array of information to generate the most effective ranking of items for each individual customer.
The simplest type of recommendation system based on discriminative models is one that relies on item popularity. This system calculates product ratings using either explicit or implicit data and can suggest products without any user-specific information. However, a major drawback of this approach is that every user is presented with the same recommendation list, resulting in a lack of personalization. Additionally, this system cannot recommend products that have never been selected or rated by other users, further limiting its effectiveness.
Content-based filtering methods rely on product descriptions and user preferences to generate recommendations. This type of system recommends products that are like those that the user has previously expressed a preference for. In this approach, product descriptions are analyzed using a feature extraction technique to convert the original descriptions into a product description vector. This vector is then used to calculate the similarities between products. The key advantage of this approach is that the products recommended for each user are independent of other users. Additionally, unlike popularity-based models, this approach can recommend new products before they have been established as popular by other users.
The final method for generating recommendations is collaborative filtering. In essence, this approach recommends products that have been favored by other users with similar tastes and behaviors. The key presumption underlying this approach is that individuals who have exhibited a preference for similar products in the past are likely to continue to exhibit a preference for similar products in the future. With this method, products can be recommended without the need for any analysis of the product itself. The most used algorithm for collaborative filtering is Matrix factorization, which operates by representing users and items in a lower dimensional latent space. To achieve this, the user-item interaction matrix is decomposed into the product of two lower dimensionality rectangular matrices.
In contemporary practice, an increasing number of companies are utilizing a hybrid system that combines content-based and collaborative methods to deliver more precise results than either method could achieve independently.
The AI Evolution
In contrast to discriminative models, generative recommendation system models generate new items or content based on user preferences and other relevant data. Several examples of generative recommendation system models include generative retrieval, generative adversarial networks (GANs), and large language models for generative recommendation. By analyzing user preferences and other applicable data, these models can produce highly customized product recommendations for clients, resulting in more tailored and relevant recommendations.
Generative Retrieval is a model that comprises a two-stage approach involving the training of a dual-encoder model for embedding user and item data, followed by a generative model that generates recommendations based on these embeddings. This approach has demonstrated encouraging results in generating personalized recommendations.
Generative AI represents a potent tool for generating highly personalized and relevant product recommendations by analyzing customer preferences and purchase history. These models integrate both collaborative and content-based filtering methods, resulting in more precise and comprehensive recommendations.
Generative Adversarial Networks (GANs) represent a type of deep learning model that can be leveraged in the development of recommendation systems. GAN-based models can generate new items or content based on user preferences and other relevant data and have demonstrated encouraging results in mitigating data noise issues[2].
User-Specific Feature-Based Similarity Models represent a class of non-collaborative user modeling techniques that leverage item features to generate personalized recommendations for users[3]. This approach can prove particularly valuable in cases where there is a dearth of preference history for the items in question, enabling the recommendation of new items to suitable users.
Interpretable Deep Generative Recommendation Models represent a model that seeks to elucidate users' intrinsic preferences to enhance recommendation performance. This model utilizes a deep generative model capable of generating new items or content based on user preferences and other relevant data, while also providing interpretability of the resulting recommendations.
In general, generative recommendation system models offer a valuable means of generating new items or content based on user preferences and other relevant data. These models can be especially beneficial in scenarios where there is a paucity of preference history for specific items or when personalized recommendations are desired.
In essence, businesses should consistently strive to enhance their personalization technologies to optimize their customers' experience. Generative recommendation system models serve as a potent tool for generating new recommendations by analyzing customer preferences, purchase history, and other relevant data, resulting in highly customized and relevant product recommendations. By leveraging both collaborative and content-based filtering methods, these models can generate more accurate and comprehensive recommendations. By capitalizing on the strengths of both approaches, businesses can offer highly personalized product suggestions that cater to a diverse spectrum of customer preferences and behaviors.
Key Takeaways
1. recommendation systems are a crucial tool for businesses seeking to deliver personalized product recommendations to their customers.
2. Different types of recommendation systems exist, including content-based filtering, collaborative filtering, and generative recommendation system models.
3. Hybrid approaches that combine multiple methods are also becoming increasingly common.
4. Generative recommendation system models, such as Generative Retrieval, Generative Adversarial Networks (GANs), and Interpretable Deep Generative Recommendation Models, offer a powerful means of generating highly personalized and relevant product recommendations by analyzing customer preferences and other relevant data.
[1] https://developers.google.com/machine-learning/recommendation/collaborative/matrix
[2] https://link.springer.com/article/10.1007/s40747-020-00212-w
[3] https://dl.acm.org/doi/10.1145/3453187.3453316