A sequential recommender system is designed to provide personalized recommendations based on the order in which user interactions occur over time. Instead of looking only at static user data or profiles, these systems take the sequence of events, such as clicks, purchases, or views, into account. This temporal context allows the system to understand user preferences better by recognizing patterns and trends in behavior as they evolve. For instance, if a user watches a series of cooking videos, a sequential recommender might prioritize suggesting related content, like specific recipes or cooking gear, tailored to that earlier activity.
The core of a sequential recommender system often relies on techniques like Markov models, recurrent neural networks (RNNs), or Transformers. These models are designed to handle sequences effectively by analyzing the transitions between items. For example, if a user first views a product description, then adds it to a cart, and later completes the purchase, the system tracks this progression. By understanding this flow, it can make more relevant suggestions, like recommending complementary products or similar items that other users bought after completing that sequence.
In practice, platforms like streaming services or e-commerce websites utilize sequential recommenders to enhance user experience. For instance, Netflix might analyze the order in which a user watches shows to suggest new series or movies that fit their viewing habits. Similarly, an online store could use sequence data to determine which items to display on the homepage based on the user's recent browsing patterns. This means sequential recommender systems do not just offer suggestions based on what users liked in the past, but actively learn from their latest actions to improve future recommendations.