Existing S equential R ecommendation (SR) methods have conventionally viewed historical interactions as one-dimensional sequences, often overlooking the fact that user behaviors can be multi-faceted and uncertain. Such a straightforward perspective fails to account for varied behavior patterns embedded in the historical sequences. Moving beyond adhering to singular historical sequences, we treat augmented sequences as meaningful behavior patterns and jointly optimize all sequences (augmented and original sequences) to capture diverse patterns, intricate dependencies, and uncertainties. To acknowledge and distinguish new patterns derived from the original sequence, we develop a sequential order-enhanced method to calculate the edge weight, highlighting the unique dependency relationships inherent in each individual sequence. To prevent recommendations from becoming monotonous due to similarities in augmented sequences, we apply a repulsive mechanism to sequences with similar topics/categories, ensuring a broader spectrum of suggestions. Considering the potent expressive capability of the probabilistic model, Structured Determinantal Point Processes (SDPP), in representing structures, we perceive original and augmented sequences as such structures, leading to our generic learning framework S tructured S equential Rec ommendation (SSRec), which is theoretically proved to be a structured ranking optimization criterion . Comprehensive experiments on real-world datasets demonstrate SSRec’s distinct advantages over state-of-the-art models in terms of both diversity and accuracy.