Consensus-Driven Group Recommendation on Sparse Explicit Feedback: A Collaborative Filtering and Choquet-Borda Aggregation Framework

TL;DR

This paper introduces a hybrid group recommendation framework that combines collaborative filtering, fuzzy aggregation, and Choquet integrals to improve consensus, fairness, and robustness in sparse data scenarios.

Contribution

It proposes a novel combination of similarity measures, Borda Count, and Choquet integral for stable, fair, and consensus-driven group recommendations without relying on social data.

Findings

Improves group consensus and satisfaction in sparse rating data

Enhances fairness and robustness in group recommendations

Maintains a balanced level of novelty and stable social behavior

Abstract

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.