<ul data-eligibleForWebStory="true"><li>The research paper introduces novel loss formulations, RG$^2$ and RG$^	imes$, to address the computational overhead and scalability issues associated with Softmax (SM) Loss in ranking tasks.</li><li>The RG$^2$ Loss and RG$^	imes$ Loss are derived through Taylor expansions of the SM Loss and reveal connections between different ranking loss paradigms.</li><li>The proposed losses are integrated with the Alternating Least Squares (ALS) optimization method to provide convergence rate analyses and generalization guarantees.</li><li>Empirical evaluations on real-world datasets show that the new approach achieves comparable or superior ranking performance to SM Loss while accelerating convergence significantly.</li><li>The framework contributes theoretical insights and efficient tools for the similarity learning community, suitable for tasks requiring a balance between ranking quality and computational efficiency.</li></ul>

NDCG-Consistent Softmax Approximation with Accelerated Convergence

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