# Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System
## 5 Results and Discussion
It can be concluded that the 1 CR and 2 CR are associated with a higher performance when its weight is lower than the remaining systems, although it is higher than zero. The CR performs better at a higher weight and the MR performs well at a weight around one. Considering the obtained results, a possible distribution of each system has been defined as 15% for the 1 CR, 15% for the 2 CR, 45% for the CR, and 25% for the MR. Through the exploration of the hybrid RS, it could be observed that the hybridisation unavoidably results in higher computational times, although the impact in the rest of the evaluation metrics is positive, leading to a considerable increase in hit rate and a small decrease portfolio coverage. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Similarity Coefficient** & **Hit** & **Portfolio** & **Avg.** & **Std. Dev.** & **Comp.** \\  & **Matrix Size** & **Rate** & **Coverage** & **Price** & **Price** & **Time** \\ \hline
1 CR & 1000 & 1.9\% & 8.4\% & EUR 33.05 & EUR 38.59 & 60 s \\ \hline
1 CR & 5000 & 1.3\% & 8.0\% & EUR 35.30 & EUR 44.13 & 178 s \\ \hline
1 CR & All & 2.0\% & 8.2\% & EUR 34.54 & EUR 32.87 & 1058 s \\ \hline
2 CR & 1000 & 0.9\% & 8.0\% & EUR 67.55 & EUR 63.85 & 60 s \\ \hline
2 CR & 5000 & 0.9\% & 8.1\% & EUR 67.55 & EUR 63.85 & 295 s \\ \hline
2 CR & All & 2.3\% & 8.2\% & EUR 49.95 & EUR 47.37 & 974 s \\ \hline CR & 1000 & 2.1\% & 8.5\% & EUR 63.26 & EUR 69.80 & 76 s \\ \hline CR & 5000 & 2.1\% & 8.5\% & EUR 63.26 & EUR 69.80 & 290 s \\ \hline CR & All & 5.1\% & 9.4\% & EUR 52.28 & EUR 51.76 & 543 s \\ \hline MR & 1000 & 3.0\% & 6.3\% & EUR 25.56 & EUR 26.72 & 80 s \\ \hline MR & 5000 & 2.6\% & 6.2\% & EUR 17.60 & EUR 16.22 & 87 s \\ \hline MR & All & 4.6\% & 6.3\% & EUR 46.37 & EUR 55.07 & 88 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of similarity coefficient matrix size. Figure 7: Impact of the variation of the relative weight of each individual model in the hybrid model hit rate. Through the comparison of the various combinations evaluated through the training sample, the final hybrid RS was defined as using the transactional dataset including the three most recent days, intermediate similarity coefficient matrices of size 1000 for the 1 CR and 2 CR, and the full matrices for the CR and MR, combined using a weighted parallelised design, attributing the weights of 15% to the 1 CR, 15% to the 2 CR, 45% to the CR and 25% to the MR. Our study concluded that the 1 CR and 2 CR are associated with a higher performance when its weight is lower than the remaining systems. The CR performs better at a higher weight and the MR performs well at a medium weight. The comparison with the baseline was performed using the recommendations dataset. Selecting a random sample containing 1000 cold-start users from the recommendations dataset, the testing sample, the first six recommendations were compared with the customer's actual next purchase, thus extracting the evaluation metrics presented in Table 5. The same testing sample was used to obtain recommendations from the developed hybrid RS, resulting in the evaluation metrics presented in Table 6. It can be concluded that the developed hybrid RS resulted in over a five-fold increase in the hit rate, at the cost of a decrease of portfolio coverage of almost four-fold. The final RS presented a higher average price and standard deviation of price, indicating a possible tendency in the previously adopted RS of selecting products from a particular price range.