Recommender Systems: Hybrid Recommender
Michael Hahsler
Thu May 26 09:45:59 2016
This work is licensed under the Creative Commons Attribution 4.0 International License. For questions please contact Michael Hahsler.
library(recommenderlab)## Loading required package: Matrix
## Loading required package: arules
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## Attaching package: 'arules'
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## The following objects are masked from 'package:base':
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## abbreviate, write
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## Loading required package: proxy
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## Attaching package: 'proxy'
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## The following object is masked from 'package:Matrix':
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## as.matrix
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## The following objects are masked from 'package:stats':
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## as.dist, dist
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## The following object is masked from 'package:base':
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## as.matrix
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## Loading required package: registrydata("Jester5k")
Jester5k## 5000 x 100 rating matrix of class 'realRatingMatrix' with 362106 ratings.train <- Jester5k[1:4000,]
test <- Jester5k[4001:5000,]Create a Simple Hybrid Recommender
Create individual recommender models
rec_pop <- Recommender(train, "POPULAR")
rec_ib <- Recommender(train, "IBCF")
rec_svd <- Recommender(train, "SVD")Save recommender models for later so we do not have to rebuild them
save(rec_pop, rec_ib, rec_svd, file = "models.rda")Load models
load("models.rda")Create predictions
pred_pop <- predict(rec_pop, test[1,], type = "ratings")
pred_ib <- predict(rec_ib, test[1,], type = "ratings")
pred_svd <- predict(rec_svd, test[1,], type = "ratings")
as(pred_pop, "list")## $u3464
## j1 j2 j3 j4 j6 j9
## 1.85183148 1.19389908 1.10918984 -0.65788120 2.51456824 0.20680256
## j10 j23 j24 j28 j30 j33
## 2.11833610 1.02223886 -0.83023690 2.32250843 0.39708705 -0.61873696
## j34 j37 j41 j43 j44 j47
## 1.80190535 -0.65830029 0.54867828 -0.04515578 -1.44647043 2.35512756
## j52 j55 j57 j58 j60 j63
## 0.76086130 1.29799243 -1.27561833 -3.08934627 0.42085267 1.04511250
## j64 j67 j71 j72 j73 j74
## 0.16941011 -0.01429141 0.01756387 3.48418582 1.74157059 -0.82959757
## j75 j76 j77 j78 j79 j80
## 0.39058276 2.99258748 1.22500213 2.29307587 0.62723250 1.64144929
## j81 j82 j83 j85 j86 j87
## 2.32469570 1.69325195 2.63240911 1.57674141 0.92069425 2.47337066
## j88 j89 j90 j91 j92 j93
## 2.67550238 4.13708907 1.05881595 2.61371288 1.89517187 3.07659495
## j94 j95 j96 j97 j98 j100
## 1.54942247 1.53353024 2.06698347 2.24605235 1.51132359 1.93065904as(pred_ib, "list")## $u3464
## j1 j2 j3 j4 j6 j9
## -0.9449249 1.3049727 -0.5044573 -1.2374976 3.5384481 -3.3740607
## j10 j23 j24 j28 j30 j33
## 1.3709456 -0.7479066 -2.6690650 2.3321870 -0.8367929 -2.3007447
## j34 j37 j41 j43 j44 j47
## 3.7453982 -3.2564195 -1.0892991 -2.7145201 -3.1107662 3.0489365
## j52 j55 j57 j58 j60 j63
## 2.9396625 -1.8361709 -3.0119828 -2.6633603 -4.1084304 -1.2760335
## j64 j67 j71 j72 j73 j74
## -3.4415371 -2.1871735 -2.8969758 2.3779717 2.7666442 -1.7621165
## j75 j76 j77 j78 j79 j80
## -1.0288039 3.3843211 -2.1076633 1.9623620 -0.8855348 3.9306994
## j81 j82 j83 j85 j86 j87
## 3.1701861 -1.5494428 4.3883344 1.5848156 -3.2009100 2.9011096
## j88 j89 j90 j91 j92 j93
## 2.0598972 2.5154311 -2.3317193 2.8124820 1.3239539 2.7568104
## j94 j95 j96 j97 j98 j100
## -2.5064341 -1.9878839 0.4196987 0.6772776 3.6669779 3.7103185as(pred_svd, "list")## $u3464
## j1 j2 j3 j4 j6 j9 j10
## 1.2162797 3.3911403 1.4208575 2.2676158 2.0994152 1.9258811 0.7285243
## j23 j24 j28 j30 j33 j34 j37
## 0.8967785 1.4281832 1.5052150 1.6089836 1.2546823 3.7139939 0.7354096
## j41 j43 j44 j47 j52 j55 j57
## 2.3523378 2.0443028 0.7625648 2.0214761 3.1681920 1.6574447 0.5642851
## j58 j60 j63 j64 j67 j71 j72
## 0.3752455 0.3925035 1.0169193 0.2855671 2.2769714 0.7676735 2.1696032
## j73 j74 j75 j76 j77 j78 j79
## 1.5977747 0.6011404 0.8200133 1.7698638 0.9651867 1.4886416 1.2758317
## j80 j81 j82 j83 j85 j86 j87
## 1.9456112 1.9217651 0.7913879 2.1343133 1.5578134 0.6719209 1.5642772
## j88 j89 j90 j91 j92 j93 j94
## 1.0111921 1.6824834 0.4780608 1.3664298 1.2034388 1.4178410 0.8320279
## j95 j96 j97 j98 j100
## 0.7395626 1.0744937 1.1873282 2.7289559 2.3558569Aggregate predictions
pred_hybrid <- rbind(
as(pred_pop, "matrix"),
as(pred_ib, "matrix"),
as(pred_svd, "matrix")
)
pred_hybrid## j1 j2 j3 j4 j5 j6 j7 j8
## u3464 1.8518315 1.193899 1.1091898 -0.6578812 NA 2.514568 NA NA
## u3464 -0.9449249 1.304973 -0.5044573 -1.2374976 NA 3.538448 NA NA
## u3464 1.2162797 3.391140 1.4208575 2.2676158 NA 2.099415 NA NA
## j9 j10 j11 j12 j13 j14 j15 j16 j17 j18 j19 j20 j21 j22
## u3464 0.2068026 2.1183361 NA NA NA NA NA NA NA NA NA NA NA NA
## u3464 -3.3740607 1.3709456 NA NA NA NA NA NA NA NA NA NA NA NA
## u3464 1.9258811 0.7285243 NA NA NA NA NA NA NA NA NA NA NA NA
## j23 j24 j25 j26 j27 j28 j29 j30 j31 j32
## u3464 1.0222389 -0.8302369 NA NA NA 2.322508 NA 0.3970870 NA NA
## u3464 -0.7479066 -2.6690650 NA NA NA 2.332187 NA -0.8367929 NA NA
## u3464 0.8967785 1.4281832 NA NA NA 1.505215 NA 1.6089836 NA NA
## j33 j34 j35 j36 j37 j38 j39 j40 j41 j42
## u3464 -0.618737 1.801905 NA NA -0.6583003 NA NA NA 0.5486783 NA
## u3464 -2.300745 3.745398 NA NA -3.2564195 NA NA NA -1.0892991 NA
## u3464 1.254682 3.713994 NA NA 0.7354096 NA NA NA 2.3523378 NA
## j43 j44 j45 j46 j47 j48 j49 j50 j51 j52
## u3464 -0.04515578 -1.4464704 NA NA 2.355128 NA NA NA NA 0.7608613
## u3464 -2.71452008 -3.1107662 NA NA 3.048937 NA NA NA NA 2.9396625
## u3464 2.04430283 0.7625648 NA NA 2.021476 NA NA NA NA 3.1681920
## j53 j54 j55 j56 j57 j58 j59 j60 j61 j62
## u3464 NA NA 1.297992 NA -1.2756183 -3.0893463 NA 0.4208527 NA NA
## u3464 NA NA -1.836171 NA -3.0119828 -2.6633603 NA -4.1084304 NA NA
## u3464 NA NA 1.657445 NA 0.5642851 0.3752455 NA 0.3925035 NA NA
## j63 j64 j65 j66 j67 j68 j69 j70 j71
## u3464 1.045112 0.1694101 NA NA -0.01429141 NA NA NA 0.01756387
## u3464 -1.276034 -3.4415371 NA NA -2.18717354 NA NA NA -2.89697576
## u3464 1.016919 0.2855671 NA NA 2.27697144 NA NA NA 0.76767348
## j72 j73 j74 j75 j76 j77 j78
## u3464 3.484186 1.741571 -0.8295976 0.3905828 2.992587 1.2250021 2.293076
## u3464 2.377972 2.766644 -1.7621165 -1.0288039 3.384321 -2.1076633 1.962362
## u3464 2.169603 1.597775 0.6011404 0.8200133 1.769864 0.9651867 1.488642
## j79 j80 j81 j82 j83 j84 j85
## u3464 0.6272325 1.641449 2.324696 1.6932520 2.632409 NA 1.576741
## u3464 -0.8855348 3.930699 3.170186 -1.5494428 4.388334 NA 1.584816
## u3464 1.2758317 1.945611 1.921765 0.7913879 2.134313 NA 1.557813
## j86 j87 j88 j89 j90 j91 j92
## u3464 0.9206942 2.473371 2.675502 4.137089 1.0588159 2.613713 1.895172
## u3464 -3.2009100 2.901110 2.059897 2.515431 -2.3317193 2.812482 1.323954
## u3464 0.6719209 1.564277 1.011192 1.682483 0.4780608 1.366430 1.203439
## j93 j94 j95 j96 j97 j98 j99
## u3464 3.076595 1.5494225 1.5335302 2.0669835 2.2460524 1.511324 NA
## u3464 2.756810 -2.5064341 -1.9878839 0.4196987 0.6772776 3.666978 NA
## u3464 1.417841 0.8320279 0.7395626 1.0744937 1.1873282 2.728956 NA
## j100
## u3464 1.930659
## u3464 3.710318
## u3464 2.355857pred_hybrid <- colMeans(pred_hybrid)
pred_hybrid## j1 j2 j3 j4 j5 j6
## 0.70772876 1.96333734 0.67519668 0.12407898 NA 2.71747716
## j7 j8 j9 j10 j11 j12
## NA NA -0.41379236 1.40593535 NA NA
## j13 j14 j15 j16 j17 j18
## NA NA NA NA NA NA
## j19 j20 j21 j22 j23 j24
## NA NA NA NA 0.39037028 -0.69037289
## j25 j26 j27 j28 j29 j30
## NA NA NA 2.05330347 NA 0.38975927
## j31 j32 j33 j34 j35 j36
## NA NA -0.55493310 3.08709915 NA NA
## j37 j38 j39 j40 j41 j42
## -1.05977009 NA NA NA 0.60390567 NA
## j43 j44 j45 j46 j47 j48
## -0.23845768 -1.26489062 NA NA 2.47518005 NA
## j49 j50 j51 j52 j53 j54
## NA NA NA 2.28957195 NA NA
## j55 j56 j57 j58 j59 j60
## 0.37308874 NA -1.24110537 -1.79248702 NA -1.09835808
## j61 j62 j63 j64 j65 j66
## NA NA 0.26199941 -0.99551997 NA NA
## j67 j68 j69 j70 j71 j72
## 0.02516883 NA NA NA -0.70391280 2.67725357
## j73 j74 j75 j76 j77 j78
## 2.03532982 -0.66352456 0.06059737 2.71559081 0.02750851 1.91469313
## j79 j80 j81 j82 j83 j84
## 0.33917647 2.50591997 2.47221563 0.31173238 3.05168560 NA
## j85 j86 j87 j88 j89 j90
## 1.57312348 -0.53609829 2.31291917 1.91553058 2.77833451 -0.26494750
## j91 j92 j93 j94 j95 j96
## 2.26420821 1.47418821 2.41708214 -0.04166125 0.09506966 1.18705861
## j97 j98 j99 j100
## 1.37021941 2.63575245 NA 2.66561149Create a top-N list from the aggregated ratings
pred <- getTopNLists(as(rbind(pred_hybrid), "realRatingMatrix"))
as(pred, "list")## $pred_hybrid
## [1] "j34" "j83" "j89" "j6" "j76" "j72" "j100" "j98" "j80" "j47"Compare with the results by the individual recommenders
as(getTopNLists(pred_pop), "list")## $u3464
## [1] "j89" "j72" "j93" "j76" "j88" "j83" "j91" "j6" "j87" "j47"as(getTopNLists(pred_ib), "list")## $u3464
## [1] "j83" "j80" "j34" "j100" "j98" "j6" "j76" "j81" "j47" "j52"as(getTopNLists(pred_svd), "list")## $u3464
## [1] "j34" "j2" "j52" "j98" "j100" "j41" "j67" "j4" "j72" "j83"