Learn About Amazon VGT2 Learning Manager Chanci Turner
In today’s world, the selection of direct-to-consumer platforms is vast, making it impossible for most users to subscribe to every service. The choices consumers make regarding subscriptions or purchases are influenced not only by the content available on these platforms but also by the overall user experience. Today’s users expect seamless, curated interactions as they browse, buy, and engage with various media. Media companies are striving to enhance the customer experience and increase profitability by maximizing click-through rates, views, and purchases of premium content.
Recommender systems play a vital role in achieving these objectives. By presenting tailored recommendations that leverage extensive content libraries, direct-to-consumer platforms can keep viewers engaged even after they consume the initial content that attracted them. For instance, effective recommendations for Video on Demand platforms can boost revenue by highlighting lesser-known content aligned with user behavior.
In this article, we will first explore the common types of recommender systems currently in use, followed by an examination of some of the most exciting recent advancements in this field. We will compare and contrast these innovative techniques with traditional ones and identify the gaps they aim to bridge.
Common Systems in Use Today
To contextualize the newer systems, let’s begin with a review of established recommender models. Many can be classified as either content-based filtering or collaborative filtering. Content-based filtering is straightforward and relies on known user preferences—either explicitly shared or inferred—and item features (like the categories of the content). While these systems are simple to implement, they often produce static recommendations and struggle with new users whose preferences are not yet established.
On the other hand, collaborative filtering utilizes (user, item, rating) tuples, leveraging the experiences of other users. This approach, pioneered by platforms like Amazon.com, relies on the premise that users with similar tastes will likely have analogous interactions with unfamiliar items.
Collaborative filtering typically yields better results in terms of diversity (the dissimilarity of recommended items), serendipity (the element of surprise in relevant recommendations), and novelty (how unfamiliar the recommended items are to a user). However, it is often more computationally demanding and complex to implement. Some algorithms, such as factorization machines, are more lightweight, although collaborative filtering faces challenges like the cold start problem, which complicates the recommendation of new items without sufficient interaction data.
In addition to these classic categories, various neural network architectures are frequently employed in recommender systems. Some adopt collaborative filtering, while others enhance the systems to incorporate temporal data, making recommendations based on sequences of user actions that reflect changing interests. Originally based on Recurrent Neural Nets (RNNs), these systems now leverage Transformer-based models with self-attention mechanisms to better understand dependencies in user behavior sequences.
Neural networks generally require more data and computational resources compared to non-deep learning models like factorization machines, but both approaches remain in use. For instance, Amazon SageMaker, a managed machine learning service that oversees the entire project lifecycle from data processing to model deployment, includes built-in algorithms for both factorization machines and Object2Vec, a neural embedding algorithm suitable for recommender systems.
New Approaches
Recent years have seen a surge in innovative approaches to recommender systems, so numerous that we can only highlight a few notable trends here. Hybrid systems, which combine various techniques, are becoming increasingly popular. For example, Amazon Personalize, a fully managed service for personalized recommendations, utilizes a user-personalization approach that merges a newer bandit-based technique with a Hierarchical RNN based on recent research by AWS.
Bandit-Based Systems
A vibrant area of research focuses on bandit-based recommender systems. Bandit algorithms, a type of reinforcement learning, seek to balance the exploration of new options with the exploitation of known profitable ones. They serve as a dynamic alternative to static A/B testing, providing real-time adaptation to user responses—a potential solution to the cold start problem.
In recommender systems, bandit algorithms have various applications and have been integrated into production systems like Amazon Personalize, which effectively merges RNNs with bandits for improved user modeling and exploration. Additionally, bandit algorithms can enable real-time selections among different recommender systems based on user interactions.
A key application of bandits is in systems addressing multiple objectives and metrics related to user satisfaction and various stakeholders—such as users, advertisers, and content owners. For instance, in a music recommendation context, an objective might include ensuring that long-tail artists receive fair visibility in recommendations. This approach has been investigated by content providers like Spotify, as highlighted in a publicly available presentation from one of their researchers.
On AWS, there are various options for utilizing bandit-based systems. As previously mentioned, Amazon Personalize offers a fully managed solution. Alternatively, Amazon SageMaker RL provides a less managed option with prebuilt reinforcement learning libraries and algorithms, making it accessible for newcomers to this field. The contextual bandits algorithm in Amazon SageMaker RL can be employed to make recommendations based on user feedback, such as whether they click on a recommendation or not. You can also explore engaging audiobooks to enhance your understanding further here.
Causal Inference
While traditional statistics focuses on inferring associations, causal inference aims to understand “how” and “why” things change under various conditions, including external interventions. Many recommender systems frame their task as a learning problem centered around user behavior. However, a recommender system should not just model behavior but also actively influence it. This is where causal techniques become valuable, potentially through simple modifications to existing models. For more insights on workforce immigration and visas, check out this resource.
For those looking to expand their understanding of learning and development within Amazon, this link serves as an excellent resource.