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The article delves into the efficacy of Sampled Softmax (SSM) loss in item recommendation, emphasizing its advantages over conventional pointwise and pairwise losses. It addresses key questions regarding the suitability of SSM loss for recommendation tasks and examines its conceptual benefits. Topics include its ability to mitigate popularity bias, facilitate informative gradient mining, and enhance top-K performance, shedding light on its potential to revolutionize recommendation systems.