SeqRec:基于长期偏好和即时兴趣的序列推荐模型
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张岩,郭斌,王倩茹,张靖,於志文
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SeqRec: sequential-based recommendation model with long-term preference and instant interest
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Yan ZHANG,Bin GUO,Qian-ru WANG,Jing ZHANG,Zhi-wen YU
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表 2 SeqRec模型与对比模型的召回率和命中率结果对比 |
Tab.2 Comparison of recall rate and hit rate results between SeqRec model and baselines |
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数据集 | 评价指标 | R1 | R5 | R10 | H1 | H5 | H10 | Baby | Caser | 0.008 3(0.002 7) | 0.022 5(0.003 0) | 0.038 8(0.002 4) | 0.015 0(0.005 0) | 0.056 25(0.005 6) | 0.110 0(0.003 1) | DREAM | 0.003 0(0.001 0) | 0.004 3(0.000 9) | 0.033 7(0.004 4) | 0.015 0(0.005 0) | 0.031 30(0.004 0) | 0.083 75(0.009 4) | SeqRec | 0.006 3(0.001 8) | 0.044 9(0.008 4) | 0.056 9(0.009) | 0.023 1(0.004 9) | 0.080 0(0.010 4) | 0.146 2(0.015 4) | 提升幅度 | −24% | 99% | 47% | 54% | 42% | 33% | Electronics | Caser | 0.001 3(0.000 5) | 0.010 0(0.003 1) | 0.025 0(0.005 1) | 0.007 5(0.002 0) | 0.040 8(0.001 2) | 0.073 3(0.006 5) | DREAM | 0.000 5(0.000 4) | 0.011 5(0.001 5) | 0.021 5(0.001 5) | 0.005 0(0.002 0) | 0.040 8(0.009 2) | 0.076 6(0.011 2) | SeqRec | 0.001 4(0.001 0) | 0.019 7(0.007 0) | 0.027 8(0.004 7) | 0.010 0(0.003 1) | 0.047 5(0.008 2) | 0.090 0(0.013 5) | 提升幅度 | 8% | 71% | 11% | 33% | 16% | 17% |
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