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

• URL: http://arxiv.org/abs/2603.21012v1
• Date: Wed, 14 Jan 2026 03:49:35 GMT
• Title: Consensus-Driven Group Recommendation on Sparse Explicit Feedback: A Collaborative Filtering and Choquet-Borda Aggregation Framework
• Abstract summary: This paper proposes a consensus-driven hybrid group recommendation framework.<n>It integrates neighborhood-based collaborative filtering with fuzzy aggregation to support agreement, fairness, and under sparsity.<n> Experimental results on real-world datasets show that the proposed method improves group-level consensus, satisfaction, and fairness, while maintaining a balanced level of novelty.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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