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As we can see in Figure 3.5, it represents the results the experiment. FMM becomes the best performing baseline method for the TripAdvisor data, but LAM and CCM beat MF by 1%. Here, HCM performs even lower than the MF approach. Through applying the utility-based method, the UBM by applying the listwise ranking can perform well the FMM method. If they use the pointwise and pairwise ranking optimizations, then the other UBM models will fail to beat FMM. From Yahoo! Movies dataset, all methods can perform the MF method that does not consider multi-criteria ratings. To be to detail, the UBM using listwise ranking can upgrade NDGC and precision by 6.3% and 5.4% in the TripAdvisor data, and 4.1% and 8% in FMM in comparison with Yahoo! Movies data [3].

Figure 3.5 Experimental result.