COFARL: COPULA-CONDITIONED FEATURE AGGRE- GATION FOR SEQUENTIAL RECOMMENDATION WITH DEEP REINFORCEMENT LEARNING

Sequential recommendation requires models to combine heterogeneous informa- tion while balancing ranking quality and cumulative reward. We introduce Co- FARL, a framework for copula-conditioned feature aggregation with deep rein- forcement learning. A causal encoder summarizes interaction history, while a cop- ula dependence descriptor conditions the weighting of candidate feature groups. The resulting representation supports temporal ranking, ranking pretraining and continued ranking supervision during sequential decision learning. Our theoret- ical analysis characterizes fixed-descriptor parameterization, the decision value of dependence information and ranking stability under descriptor perturbations. Experiments span MovieLens recommendation, the RL4RS native simulator and expanding-window cryptocurrency asset selection. In a matched twenty-seed MovieLens study, aligned copula conditioning improves ranking over attention and all descriptor controls, with reward gains over attention, permuted copula and sequence-only models surviving multiplicity correction. Comparisons with side- information fusion methods demonstrate higher simulated reward while retaining most of the strongest comparator’s mean ranking quality. RL4RS and financial experiments further characterize the framework across recommendation environ- ments and chronological out-of-sample decisions. Together, these results support dependence-conditioned aggregation as a useful component of sequential recom- mendation systems.