## 4 Experiments and Analysis of Results
### Ablation Experiment  
The MGMI-CEHCN model can be seen as the result of the union of different modules. In order to evaluate the validity and robustness of the proposed modules, the five main parts of the MGMI-CEHCN were deleted separately, which are \(MGMI-CEHCN^{C}\), \(MGMI-CEHCN^{P}\), \(MGMI-CEHCN^{M-G}\), \(MGMI-CEHCN^{M-I}\), and \(MGMI-CEHCN^{L}\). The five variants are further described as follows:  
\(MGMI-CEHCN^{C}\) : Deleting category information in the M-G encoder. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **P@10** & **MRR@10** & **P@20** & **MRR@20** \\ \hline \multicolumn{5}{c}{(a) Cosmetics} \\ \hline S-POP & 32.83 & 26.63 & 38.43 & 27.32 \\ SKNN & 40.22 & 30.40 & 47.63 & 30.80 \\ GRU4 Rec & 19.41 & 14.43 & 21.80 & 14.60 \\ NARM & 42.63 & 34.17 & 46.29 & 34.52 \\ SR-GNN & 44.11 & 34.59 & 48.01 & 34.96 \\ LESSR & 38.80 & 24.45 & 46.32 & 24.97 \\ S\({}^{2}\)-DHCN & 40.48 & 32.86 & 47.95 & 33.13 \\ COHHN & 47.88 & 36.38 & 53.56 & 36.79 \\ MGMI-CEHCN & 49.38 & 37.86 & 55.25 & 38.26 \\ Improv & 3.13 & 4.07 & 3.06 & 3.99 \\ \hline \multicolumn{5}{c}{(b) Dginetica-buy} \\ \hline S-POP & 25.51 & 18.82 & 25.91 & 19.84 \\ SKNN & 45.68 & 20.24 & 55.76 & 21.10 \\ GRU4 Rec & 22.04 & 11.32 & 27.88 & 11.73 \\ NARM & 46.56 & 21.76 & 57.34 & 23.27 \\ SR-GNN & 45.74 & 21.32 & 56.80 & 22.87 \\ LESSR & 47.88 & 20.82 & 61.35 & 22.64 \\ S\({}^{2}\)-DHCN & 45.89 & 21.08 & 54.91 & 22.03 \\ COHHN & 50.57 & 24.81 & 64.02 & 25.76 \\ MGMI-CEHCN & 52.25 & 26.56 & 65.60 & 27.47 \\ Improv & 3.32 & 7.05 & 2.47 & 6.64 \\ \hline \multicolumn{5}{c}{(c) Amazon} \\ \hline S-POP & 34.60 & 31.96 & 38.03 & 32.19 \\ SKNN & 61.55 & 46.07 & 64.23 & 46.30 \\ GRU4 Rec & 55.43 & 51.43 & 56.41 & 51.70 \\ NARM & 63.21 & 57.07 & 65.38 & 57.23 \\ SR-GNN & 65.32 & 57.46 & 65.83 & 57.89 \\ LESSR & 62.48 & 56.53 & 64.18 & 56.69 \\ S\({}^{2}\)-DHCN & 58.67 & 49.86 & 60.47 & 50.03 \\ COHHN & 65.32 & 58.78 & 67.69 & 59.01 \\ MGMI-CEHCN & 65.81 & 59.39 & 68.39 & 59.59 \\ Improv & 0.75 & 1.03 & 1.03 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different models with public datasets (unit: %) (adapted from ref. [46]). (a) Cosmetics, (b) Dginetica-buy, (c) Amazon. The highest performances are shown in the table as underlined. \(MGMI-CEHCN^{P}:\) Delete the price information from the M-G encoder. \(MGMI-CEHCN^{M-G}:\) Delete the model of the M-G encoder module of the MGMI-CEHCN. \(MGMI-CEHCN^{M-I}:\) Delete the model of the M-I encoder module of the MGMI-CEHCN. \(MGMI-CEHCN^{L}:\) Delete the model for the joint contrast enhancement module. The experimental results are shown in Table 3, the MGMI-CEHCN model consistently outperformed the five variants on the Cosmetics, Diginetica-buy, and Amazon datasets. This means that each component played an active role in the recommendation process of the MGMI-CEHCN model, thus validating the effectiveness of the methodology of this paper. Overall, \(MGMI-CEHCN^{C}\) outperformed \(MGMI-CEHCN^{P}\), and \(MGMI-CEHCN^{P}\) outperformed \(MGMI-CEHCN^{M-G}\), suggesting that embedding two types of granularity information, price and category, into the items can indeed improve the accuracy of the recommender system. However, the price information of each item appeared to be more important compared to the category information, and we believe that the price information is usually more real-time and sensitive. Users may check the current price situation before making a purchase, whereas category information may have relatively less impact on the purchase decision. Therefore, price information is more likely to attract users' attention and affect decisions in session recommendation. When the joint comparison learning module was removed, this produced a significant performance degradation in all cases.