Sequential recommender systems are a type of recommendation algorithm that focuses on the order of user interactions to predict future actions. Unlike traditional methods that treat user behavior as a static set of preferences, these systems analyze sequences of actions—such as clicks, purchases, or views—to identify patterns over time. For example, in an e-commerce setting, a sequential model might predict that a user who buys a phone case after purchasing a smartphone is more likely to buy screen protectors next, rather than unrelated items. This approach is particularly useful in scenarios where the timing and order of interactions matter, such as music streaming platforms recommending songs based on listening history or video services suggesting the next episode in a series.
These systems typically process user interaction sequences using machine learning models designed to handle temporal dependencies. Common techniques include recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or transformer-based architectures like SASRec (Self-Attentive Sequential Recommendation). For instance, a model might take a user’s sequence of movie ratings—["Movie A", "Movie B", “Movie C”]—and encode each item into a vector, then use attention mechanisms to weigh the importance of each past interaction when predicting “Movie D” as the next recommendation. Training often involves maximizing the likelihood of the next item in a sequence given previous items, using loss functions like cross-entropy. Developers might preprocess data by splitting user histories into sliding windows (e.g., the last 10 interactions) to create input-target pairs for training.
Practical applications include session-based recommendations (e.g., suggesting products during a shopping session) or personalized content feeds in social media. However, challenges exist. Handling long sequences efficiently can strain computational resources, leading to trade-offs between model complexity and latency. For example, transformers scale quadratically with sequence length, making them costly for very long histories. Noise in the data—like accidental clicks—can also skew predictions, requiring robust preprocessing or noise-resistant architectures. Additionally, cold-start scenarios (e.g., new users with minimal history) may require hybrid approaches combining sequential and non-sequential signals. Despite these hurdles, sequential models are widely adopted in industry due to their ability to capture evolving user preferences more accurately than static methods.