A sequential recommender system improves recommendations over time by learning from user interactions and adjusting its model based on the sequence and context of those interactions. Unlike traditional recommenders that may rely heavily on static user profiles or item-based metrics, sequential systems track the order in which users engage with items. This allows them to capture patterns that reflect user behavior over time, enhancing their ability to provide relevant suggestions.
For example, consider a music streaming service. A user might frequently listen to upbeat songs in the morning and calming music in the evening. A sequential recommender can analyze these listening patterns and adjust its recommendations accordingly. If the system notices a user has started a new playlist with several energetic tracks in the morning, it might recommend similar upbeat songs or artists that fit that evolving pattern. This context-awareness ensures that recommendations are not just based on general user preferences but are timely and relevant to the moment.
Over time, as the system collects more data about user interactions, it can refine its algorithms. This might include employing techniques like recurrent neural networks (RNNs) or Markov models to predict future interactions based on past sequences. If a user consistently shows interest in specific genres or artists that follow particular trends, the system can adapt to incorporate these preferences dynamically. In this way, a sequential recommender continues to improve its effectiveness, ensuring that recommendations are aligned with the natural shifts in user behavior and preferences over time.