Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 68

To compare this approach with single- as well as multi-criteria rating systems, they implemented the approach with some different research result proposed by different researchers. Those are MF, 2016 Hybrid AE [23] and multi-criteria recommendation techniques: 2011 Liwei Liu [13], 2017 Learning [22], three approaches from [27] (2017 CCC, 2017 CCA, and 2017 CIC). Certain procedures are used on all the functioning datasets. The results are shown in Tables 3.1 to 3.3. Conventional matrix factorization got the most ever loss in terms of MAE, GIMAE, and GPIMAE with values 1.2077, 1.3055, and 0.8079, respectively, as shown in Table 3.1. In terms of mean absolute error and F1, 2017 Pref Learning carry out superior to existing single and multi-criteria rating techniques. However, this method performs well in all the existing methods. It can be seen that MF got the maximum loss and least F1. Their preferred extended stacked autoencoder approach went beyond all the methods sufficiently in various evaluation metrics, as shown in Table 3.2. Similar trends are also found on the other datasets, YM 10-10 and YM 20-20 in Tables 3.3 and 3.4, respectively [4].