Learning in harmony: contrastive, sequential and meta-learning for robust multi-stakeholder recommendation

Purpose Digital platforms across the globe and specifically in India increasingly demand recommender systems that elevate user experience, empower producers and reassure platform custodians simultaneously. We therefore tackle data sparsity, cold-start fragility, non-linear preference formation and opaque decisioning without compromising creator exposure as well as systemic health. Design/methodology/approach Heterogeneous ensemble with inclusion of contrastive self-supervision for solving sparsity, transformer-based sequence modeling to solve behavioral evolution, and a TabNet deep tabular learner for heterogeneous features is coordinated by a context-aware meta-learner. Training optimizes three interconnected objectives, maintaining user fidelity, enhancing producer opportunity and ensuring overall system balance. Stakeholder telemetry is emitted each validation pass through an automated pipeline that acts as a deployment gate. Findings On MovieLens-100K, 1M and 20M, the ensemble achieved user-centric Root mean square error of 0.9473, 0.8942, and 0.8651, respectively, with normalized discounted cumulative gain consistently above 0.98. Producer-centric metrics climb from an exposure parity of 0.0059 and catalog reach of 0.0059 on MovieLens-100K to 0.0421 and 0.0385 on MovieLens-20M, with Hit Rate@10 undefined on the smallest split but measurable at 0.0245 and 0.0389 on the 1M and 20M datasets. System-centric evaluation maintains bias-free coverage above 0.996 and interaction diversity between 0.3666 and 0.4123, while stability is only reported for the 1M and 20M datasets where temporal windows are populated. Multi-stakeholder optimization yields combined scores of 0.4304, 0.5127 and 0.5938 across datasets, proving coordinated stakeholder benefit. Originality/value This study introduces the multi-stakeholder recommender system that is benchmarked on the complete MovieLens dataset, embedding producer and system telemetry inside the optimization loop. The use case of the solution creates by us lies in the domain of over-the-top (OTT), ed-tech and commerce ecosystems pursuing responsible artificial intelligence.