Sequential recommender systems are algorithms designed to provide recommendations based on the order in which interactions or events occur. Unlike traditional recommender systems, which may recommend items based solely on user preferences or item similarities, sequential recommenders take into account the sequence of actions or choices made by users over time. For instance, if a user watches a series of movies on a streaming platform, a sequential recommender will analyze the order of those movies to suggest what to watch next, recognizing that viewing behaviors often follow specific patterns.
These systems typically operate by modeling user behavior as a time-ordered sequence. Techniques employed may include Markov models or learning methods that capture transitions from one action to another. For example, in an e-commerce setting, if a customer first views a product, then adds it to their cart, and finally purchases it, a sequential recommender might identify that sequence and suggest related products based on those actions. This approach helps improve user experience by providing recommendations that feel more relevant and timely, closely aligned with the user's current context.
An illustration of this can be seen in music streaming services, where the songs a user listens to consecutively influence the next song recommendation. If a user often goes from upbeat tracks to slower ballads in their listening history, the system can learn this preference and suggest tracks accordingly. By tracking these patterns over time, sequential recommender systems enhance recommendation accuracy, ensuring that users feel the system is in tune with their evolving preferences.