Preference Learning and Hybrid Combinatorial Optimization for Intelligent Group-Buying Platforms: A Prototype-Calibrated Study of SmartBuy Connect

Group-buying platforms require the joint treatment of individual user relevance and hard operational constraints, including minimum group size, lot capacity, spending limits, product availability, and simultaneous participation limits. This paper presents Preference-Aware Hybrid Combinatorial Group Optimization (PA-HCGO), a prototype-calibrated decision framework that integrates implicit feedback preference learning with constrained user–lot assignment. The contribution is not a new recommender architecture or a new integer programming solver, but a system-level integration of learned user–lot utility and hard group-buying feasibility constraints inside the SmartBuy Connect prototype. The empirical part uses an anonymized prototype dataset containing 200 products, 150 users, 150 lots, 4000 user events, and 500 orders. The evaluation is explicitly divided into three tracks: observed temporal recommendation testing, a calibrated counterfactual pre-activation scenario, and synthetic scalability tests generated from prototype-calibrated distributions. In the observed transactional test subset, which contains 40 held-out transactional items from 36 users, the PA-PREF preference layer achieved Recall@10 = 0.3611 and NDCG@10 = 0.1319 using user–category profiles available at the end of the training interval. Although PA-PREF obtained the highest point estimate, its advantage over the content-only baseline was not statistically separable at the 95% level on this small transactional subset. The result is therefore treated as preliminary. For general engagement, the popularity baseline remained stronger, indicating that the proposed preference model is more useful for transactional intent prediction than for all activity types. In the calibrated counterfactual pre-activation scenario, PA-HCGO activated 117 of 150 lots, compared with 85 lots under independent greedy assignment. This scenario is a constructed pre-activation setting rather than an observed historical platform state. In synthetic scalability tests, the method remained computationally feasible up to 5000 users and 1000 lots, with a mean solve time of 4.7174 s in the prototype implementation. The results suggest that learned user–lot utility can improve constrained group formation, but the evidence should be interpreted as prototype-calibrated rather than as proof of industrial-scale business effectiveness.