Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 71

The outputs are revealed in Figure 3.4. If we take a sincere look, then we will find that the data tags on above of every bar present the rate of development by HCM in correspondence with other methods. In association with algorithms, biased MF represented the outcomes that are generated by the biased MF algorithm. The present results formed on the aggregation-based approach that takes benefits of multiple-criteria ratings. The aggregation is the hybrid model that merges user-specific aggregation models with item-specific aggregation models. In this paper, the proposed models are FCM, PCM, and HCM. In PCM, they choose the most authoritative criteria as contexts using information gain. They tried many selections and combinations here and represented the best selections in this research work [19].

First, biased MF does not require more details like multi-criteria ratings or contexts. So, for this reason, it is the worst model here. As FCM carries outpour efficiency than the Agg method in the TripAdvisor dataset, so applying contexts as criteria preference will not be inadequate choice every time. Choosing the most influential criteria, PCM performs better Agg in those two datasets. Eventually, they observed, HCM is the finest predictive model with the shortest mean absolute error. It has enough to provide remarkable improvements compared with other models and depends on the statistical paired t-test. To be more specific, it is fit to acquire 4.7% and 8.7% improvements in balancing with the aggregation model, 6.7% and 6.9% improvements compared with the FCM, in the TripAdvisor and Yahoo! Movies datasets, respectively. They have proved that HCM performs better than PCM in this experiment [19].

Figure 3.4 Result comparison.