融合用户感知和多因素的兴趣点推荐

卢巧杰,王楠,李金宝,李坤

Point-of-interest recommendation integrating user perception and multi-factor

Qiao-jie LU,Nan WANG,Jin-bao LI,Kun LI

表 2 UPMF模型与基线对比模型在Gowalla和Yelp数据集上的P@N、R@N和NDCG@N性能比较

Tab.2 Performance comparison of P@N, R@N and NDCG@N between UPMF model and baselines on Gowalla and Yelp datasets

对比模型 Gowalla Yelp P@10 P@20 R@10 R@20 NDCG@10 NDCG@20 P@10 P@20 R@10 R@20 NDCG@10 NDCG@20 TopPopular 0.0192 0.0146 0.0176 0.0270 0.0088 0.0079 0.0077 0.0073 0.0095 0.0185 0.0075 0.0073 PFMMGM 0.0240 0.0207 0.0258 0.0442 0.0140 0.0144 0.0162 0.0135 0.0242 0.0402 0.0175 0.0152 LMFT 0.0315 0.0269 0.0303 0.0515 0.0157 0.0150 0.0183 0.0163 0.0264 0.0457 0.0194 0.0176 iGLSR 0.0297 0.0242 0.0283 0.0441 0.0153 0.0145 0.0235 0.0192 0.0306 0.0529 0.0231 0.0214 GeoSoCa 0.0215 0.0195 0.0253 0.0449 0.0222 0.0206 0.0183 0.0150 0.0214 0.0340 0.0195 0.0168 MLP 0.0243 0.0215 0.0237 0.0396 0.0098 0.0127 0.0203 0.0174 0.0284 0.0157 0.0195 0.0185 Rank-GeoFM 0.0352 0.0297 0.0379 0.0602 0.0187 0.0179 0.0231 0.0198 0.0316 0.0587 0.0217 0.0214 L-WMF 0.0341 0.0296 0.0351 0.0582 0.0183 0.0178 0.0215 0.0181 0.0295 0.0542 0.0202 0.0194 LFBCA 0.0453 0.0376 0.0460 0.0742 0.0492 0.0427 0.0269 0.0228 0.0408 0.0667 0.0290 0.0255 CARA 0.0501 0.0410 0.0485 0.0792 0.0531 0.0452 0.0255 0.0219 0.0362 0.0628 0.0271 0.0239 SUCP 0.0502 0.0410 0.0520 0.0823 0.0548 0.0470 0.0286 0.0245 0.0438 0.0731 0.0304 0.0270 UPMF 0.0556 0.0445 0.0571 0.0886 0.0618 0.0519 0.0307 0.0267 0.0460 0.0784 0.0326 0.0292 Imp/% 10.76 8.53 9.81 7.65 12.77 10.43 7.34 8.98 5.02 7.25 7.24 8.15