Factorized Coupled Sequence Modeling for Robust Sequential Prediction

Sequential prediction in real-world systems often involves coupled sequence pairs, i.e. a primary action sequence and a co-evolving environment-level context sequence (e.g., app sub-interfaces and publisher sources, as opposed to item-intrinsic attributes like category or brand). Unlike item-level context, such environment-level context is susceptible to alignment noise: element-wise correspondence between the two sequences is inherently corrupted by delayed feedback and context switching. Existing approaches either ignore the context sequence or encode it jointly with actions at the element level. The former loses useful contextual signals, while the latter can introduce erroneous pairwise information and perturb the learned representations. We factorize the joint sequence model into two interacting sequence models that capture their own temporal dynamics and exchange information only at the representation level. We instantiate this design as Factorized Coupled Sequence Networks (FCSNet), with parallel sequence encoders, cross-sequence representation fusion, and dual prediction heads. Under mean-field posterior and factorized-prior assumptions, its training objective can be interpreted as an approximation to a factored evidence lower bound (ELBO) of the joint likelihood. We further introduce Conditional Contrastive Regularization (CCR), which uses similarity-adaptive weights to emphasize informative contrastive pairs under heavy-tailed sequential data. We validate FCSNet on two domains where coupled sequences with alignment noise naturally arise: e-commerce purchasing and online news browsing. Experiments on one public news benchmark and two industrial e-commerce datasets examine predictive performance and robustness to controlled alignment noise. Production deployment in an e-commerce search system further confirms real-world impact, achieving a 0.04 absolute-point CTR increase, 0.78% deal growth, and 0.64% GMV rise. Code is available in the anonymous repository at bluehttps://anonymous.4open.science/r/FCSNet-ForPublish/.