A sequential recommender system is a type of recommendation model that focuses on the order of user interactions to predict future actions. Unlike traditional recommendation systems, which often treat user behavior as a collection of unrelated events, sequential models emphasize the temporal sequence of actions. For example, in a music streaming app, such a system might analyze the order in which a user listens to songs to recommend the next track, rather than relying solely on their overall preferences. The key idea is that the sequence of past actions provides critical context for understanding what a user is likely to do next.
These systems typically use algorithms that can process ordered data, such as Markov chains, recurrent neural networks (RNNs), or transformers. For instance, a Markov chain might model the probability of transitioning from one item to another based on observed sequences, while an RNN could capture longer-term dependencies by maintaining a hidden state that evolves over time. More recently, transformer-based architectures like BERT4Rec have gained popularity because they use attention mechanisms to weigh the importance of different items in a sequence. For example, in an e-commerce setting, a transformer might identify that a user who viewed a laptop, then a mouse, and then a keyboard is more likely to purchase a laptop stand next, rather than unrelated items like clothing.
From a practical standpoint, building a sequential recommender involves handling challenges like variable-length sequences, sparse data, and real-time performance. Developers often preprocess data into fixed-length sequences or use techniques like padding and masking to handle variability. Tools like TensorFlow or PyTorch provide libraries for implementing RNNs or transformers, while frameworks like SASRec (Self-Attentive Sequential Recommendation) offer pre-built architectures. Evaluation metrics such as Hit Rate, NDCG (Normalized Discounted Cumulative Gain), or MRR (Mean Reciprocal Rank) are commonly used to measure accuracy. For example, a video platform might test whether the top recommendation in a user’s “Up Next” list aligns with their actual viewing choice. Balancing model complexity with computational efficiency—especially for large-scale systems—is critical to ensure real-time recommendations without excessive latency.