## 4 Implementation and Experiment Results
### Parameter Analysis
#### 4.3.3 Performance of the Proposed CS Item Recommender  
As mentioned in Section 3, item \(v\) is called a cold start item (CS item) if the number of ratings for \(v\) is no greater than a given threshold value \(\rho\). For simplification, most previous studies simply ignore the CS items or even remove them from the dataset. Unlike previous works, this study derives the CS item ratings from similar non-CS items using auxiliary textual information by the proposed DACR generator.  
To show the benefits of the proposed CS item recommender, the CS items' ratings under different threshold values are derived and tested. For the ease of understanding, \(\text{DACR}_{\rho}\) indicates the DACR generator is applied to the CS items that receive no greater than \(\rho\) ratings. For example, \(\text{DACR}_{4}\) means that the DACR generator generates the ratings for CS items that receive no greater than four ratings in our dataset. Table 7 summarizes six DACR models and the number of CS items that the six models will deal with. Note that \(\text{DACR}_{0}\) indicates no CS item rating generated using the proposed DACR generator. In addition to the number of CS items, CS item rating might be affected by the number of most similar non-CS movies, \(\alpha\), as shown in Equation (4). Thus, \(\alpha\) is changed from 10, 20, 30, 40 to 50 in the following discussion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MAE** & **MSE** & **RMSE** \\ \hline BaselineOnly & 0.723446 & 0.850494 & 0.922222 \\ KNNWithZScore & 0.684817 & 0.786650 & 0.886933 \\ KNNBaseline & 0.691259 & 0.783414 & 0.885107 \\ SVD & 0.650909 & 0.691885 & 0.831796 \\ SVD++ & 0.641086 & 0.688568 & 0.829800 \\ NNCF\({}_{1}\) & 0.648282 & 0.638057 & 0.798785 \\ NNCF\({}_{2}\) & 0.639093 & 0.633585 & 0.795980 \\ NNCF\({}_{3}\) & 0.602003 & 0.632619 & 0.754669 \\ NNCF\({}_{4}\) & 0.598555 & 0.630442 & 0.751286 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance for the NNCF predictors.  
Figure 6: Effect of the number of nodes in the embedding layer of the NNCF predictors.  
Figure 7 illustrates the MAE for different DACR + NNCF combinations. For example, Figure 7a shows the MAE when 6 DACR models are applied with NNCF\({}_{1}\). When no CS item rating is added (i.e., DACR\({}_{0}\)), the MAE of the proposed recommender is 0.6577. However, when the DACR\({}_{4}\) model is applied, the MAE decreases to 0.6483, a 1.45% improvement (=(0.6577 \(-\) 0.6483)/0.6483) for \(\alpha\) = 10. Moreover, when the DACR\({}_{12}\) model is applied, the MAE decreases to 0.6062, an 8.49% improvement (=(0.6577 \(-\) 0.6062)/0.6062) for \(\alpha\) = 50. Figure 7b-d shows similar trends and patterns. Table 8 summarizes the average MAE improvement compared to no CS item rating added when NNCF\({}_{1}\) to NNCF\({}_{4}\) are applied. Based on Figure 7 and Table 8, it is clear that when more CS items are added (from DACR\({}_{4}\) to DACR\({}_{12}\)), lower MAE values can be found. Besides, when \(\alpha\) increases, the MAEs for all DACR models decrease also. However, larger \(\alpha\) makes the computation time much longer when evaluating item similarity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Threshold Value \(\rho\)** & **Number of Generated CS Items** \\ \hline DACR\({}_{0}\) & 0 & None \\ DACR\({}_{4}\) & 4 & 20 \\ DACR\({}_{6}\) & 6 & 40 \\ DACR\({}_{8}\) & 8 & 73 \\ DACR\({}_{10}\) & 10 & 145 \\ DACR\({}_{12}\) & 12 & 285 \\ \hline \hline \end{tabular} An item \(v\) is defined as a CS item if the rating received is no greater than the given threshold value \(\rho\).  
\end{table}
Table 7: The summary for the six DACR models.  
Figure 7: The performance of the proposed recommender under different DACR + NNCF settings.