SMMR: Sampling-Based MMR Reranking for Faster, More Diverse, and Balanced Recommendations and Retrieval

Relevance and diversity are critical objectives in modern information retrieval (IR), particularly in recommender systems. Achieving a balance between relevance (exploitation) and diversity (exploration) optimizes user satisfaction and business goals such as catalog coverage and novelty. While existing post-processing reranking methods address this trade-off, they usually rely on greedy strategies, leading to suboptimal outcomes for large-scale tasks. To this end, we propose Sampled Maximal Marginal Relevance (SMMR), a novel sampling-based extension of MMR that introduces randomness into item selection to improve relevance-diversity trade-offs. SMMR avoids the rigidity of greedy and deterministic reranking, and achieves a logarithmic computational speedup, which allows it to scale on large candidate sets. Our evaluations on multiple real-world open-source datasets demonstrate that SMMR consistently outperforms existing state-of-the-art approaches, offering superior performance in balancing relevance and diversity. Our implementation of the proposed method is made available to support future research.