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

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2025. P. 2754–2758.

Liakhnovich K., Lashinin O., Babkin A., Pechatov M., Ananyeva M.

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 realworld 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.

Keywords: information retrieval, diversity, recommender systems, post-processing methods, candidates generation, reranking