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Table 4: Results to Brightkite.
Method Best set of the grid search
Accuracy Precision Recall F1-Score
mean std mean std mean std mean std
DeepeST-LSTM un:400, es:100, dp:0.5 0,9591 0,0018 0,9644 0,0048 0,9514 0,0025 0,9512 0,0033
DeepeST-BILSTM un:100, es:100, dp:0.5 0,9632 0,0030 0,9658 0,0038 0,9563 0,0044 0,9557 0,0041
BiTULER un:100, es:100, dp:0.5 0,9372 0,0052 0,9417 0,0055 0,9211 0,0068 0,9234 0,0066
TULVAE un:100, es:300, dp:0.5, z:300 0,9452 0,0044 0,9439 0,0062 0,9325 0,0048 0,9308 0,0054
Random Forest ne:200, md:30, mss:5, msl:1, mf:auto, bs:False 0,8717 0,0043 0,8744 0,0090 0,8431 0,0060 0,8440 0,0072
XGBoost ne:2000, md:5, gm:0, ss:0.8, cst:0.5, l1:1, l2:100 0,8769 0,0047 0,8717 0,0059 0,8481 0,0063 0,8483 0,0065
Table 5: Results to Gowalla.
Method Best set of the grid search
Accuracy Precision Recall F1-Score
mean std mean std mean std mean std
DeepeST-LSTM un:100, es:400, dp:0.5 0,9760 0,0039 0,9821 0,0027 0,9744 0,0042 0,9750 0,0039
DeepeST-BILSTM un:200, es:100, dp:0.5 0,9739 0,0038 0,9798 0,0034 0,9727 0,0040 0,9723 0,0044
BiTULER un:300, es:400, dp:0.5 0,9122 0,0050 0,9274 0,0070 0,9101 0,0060 0,9078 0,0072
TULVAE un:100, es:300, dp:0.5, z:300 0,9159 0,0085 0,9338 0,0121 0,9111 0,0096 0,9105 0,0109
Random Forest ne:400, md:30, mss:2, msl:2, mf:sqrt, bs:False 0,7047 0,0088 0,7020 0,0139 0,6841 0,0093 0,6631 0,0109
XGBoost ne:2000, md:10, gm:0, ss:0.8, cst:0.5, l1:1, l2:100 0,6545 0,0112 0,6393 0,0197 0,6327 0,0134 0,6143 0,0152
Table 6: Results to Criminal Dataset.
Method Best set of the grid search
Accuracy Precision Recall F1-Score
mean std mean std mean std mean std
DeepeST-LSTM un:100, es:400, dp:0.5 0,9188 0,0010 0,8792 0,0075 0,8283 0,0043 0,8504 0,0040
DeepeST-BILSTM un:200, es:100, dp:0.5 0,9203 0,0013 0,8826 0,0071 0,8365 0,0052 0,8564 0,0026
Random Forest ne:400, md:30, mss:2, msl:2, mf:sqrt, bs:False 0,7917 0,0002 0,6806 0,0017 0,5515 0,0010 0,5910 0,0012
XGBoost ne:2000, md:10, gm:0, ss:0.8, cst:0.5, l1:1, l2:100 0,8121 0,0008 0,6671 0,0017 0,5765 0,0015 0,6084 0,0011
preprocessing and features creation. Finally, we aim
at studying how to improve accuracy by means of
other deep learning techniques, like attention mech-
anisms.
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