在稀疏评分数据下，用协同过滤与模糊聚合提升群体推荐的共识与公平性。

Group Recommender Systems (GRS) play an essential role in supporting collective decision-making among users with diverse and potentially conflicting preferences. However, achieving stable intra-group consensus becomes particularly challenging when only sparse userID-itemID-rating data are available and no demographic, contextual, or group-level information exists. This paper proposes a consensus-driven hybrid group recommendation framework that integrates neighborhood-based collaborative filtering with fuzzy aggregation to support agreement, fairness, and robustness under sparsity. A composite similarity measure, CBS (Combined Similarity), is derived from two enhanced similarity metrics introduced in prior work: a geometry-based measure that captures rating-pattern structure, and an uncertainty-aware measure that models belief, evidence, and disagreement in sparse co-rating contexts. This combination provides more stable estimation of missing ratings and supports consensus-oriented neighborhood construction. Candidate items are generated by merging per-user top-N predictions and further enriched using the Borda Count mechanism to mitigate skewed rating distributions and reinforce group-level agreement. Final group ratings are computed using the Choquet integral, which flexibly captures heterogeneous user influence while preserving fairness and supporting consensus formation. Experimental results on real-world datasets with different rating distributions show that the proposed method improves group-level consensus, satisfaction, and fairness, while maintaining a balanced level of novelty. Although the model does not rely on social information, its evaluation using trust-aware novelty measures indicates stable behavior in socially structured environments.