融合用户信任和影响力的top-N推荐算法

张雪峰,陈秀莉,僧德文

Top-N recommendation algorithm combining user trust and influence

Xue-feng ZHANG,Xiu-li CHEN,De-wen SENG

表 3 Top-N项目推荐对比实验结果

Tab.3 Top-N item recommendation comparison experiment results

数据集 方法 N=5 N=10 $P@N$ $F1@N$ ${\rm{NDCG}}@N$ $P@N$ $F1@N$ ${\rm{NDCG}}@N$ Epinions MostPop 0.011 690 0.012 98 0.012 334 0.009 171 0.013 05 0.016 238 GBPR 0.009 353 0.011 03 0.012 296 0.007 560 0.011 11 0.016 095 FISM 0.011 470 0.013 07 0.012 808 0.009 020 0.013 15 0.016 361 FST 0.011 790 0.013 30 0.013 988 0.009 187 0.013 28 0.016 930 FSTID- 0.012 310 0.014 02 0.014 355 0.010 240 0.014 59 0.017 588 FSTID 0.012 430 0.014 15 0.014 470 0.010 480 0.014 76 0.017 832 Ciao MostPop 0.026 770 0.024 36 0.025 906 0.021 420 0.026 62 0.033 443 GBPR 0.022 280 0.020 63 0.022 319 0.018 270 0.021 16 0.028 759 FISM 0.027 040 0.024 95 0.026 185 0.021 410 0.026 87 0.032 510 FST 0.027 410 0.025 23 0.027 240 0.021 740 0.027 20 0.034 910 FSTID- 0.028 300 0.026 44 0.027 389 0.023 290 0.029 14 0.035 503 FSTID 0.029 240 0.026 82 0.027 634 0.023 610 0.029 50 0.035 932 FilmTrust MostPop 0.417 000 0.409 50 0.409 529 0.350 300 0.451 80 0.538 924 GBPR 0.412 400 0.405 10 0.372 923 0.347 000 0.445 80 0.500 997 FISM 0.417 100 0.408 70 0.413 404 0.350 300 0.451 60 0.540 511 FST 0.419 100 0.409 90 0.419 351 0.351 400 0.452 10 0.545 109 FSTID- 0.419 800 0.411 60 0.426 273 0.353 200 0.454 10 0.547 688 FSTID 0.420 500 0.412 40 0.427 569 0.353 300 0.454 80 0.551 260