# An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory
## 4 Results and Discussion
### Results  
#### 4.3.1 Recommendation Quality  
First, in Section 3.2, we conducted the experiment on datasets with the training-testing ratio 90-10% to find the appropriate dimension for embedding vectors. For the movie dataset, as shown in Figure 7a, the lowest RMSE occurred when the dimension of embedding vectors was between 50 and 100, which does not depend on the experimental algorithm to be evaluated. Meanwhile, considering the number of dimensions of a movie's own attributes, such as classes, user preferences and so on, we chose 64 to be the embedding's dimension for movies. Similarly, as shown in Figure 7b, we chose 32 to be the embedding vector's dimension for music. Moreover, the second experiment was conducted to discuss the selection of K-neighbors for RICF. The effects of K-neighbors in RICF are compared with that in traditional itemCF, and the experiment results are shown in Figure 8. We found that only a few K-neighbors are required to achieve good results in RICF and we speculate that the reason is related to the embedding model trained to optimize based on the rating target, because embeddings \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MovieTweetings-100k** & **MovieTweetings-Latest** & **DigitalMusic** \\ \hline Number of users & 16,554 & 68,332 & 478,235 \\ Number of items & 10,506 & 35,931 & 266,414 \\ Number of ratings & 100,000 & 876,673 & 836,006 \\ Rating range & 1–10 & 1–10 & 1–5 \\ Time range & 28/02/2013 to 01/09/2013 & 28/02/2013 to 10/07/2020 & 20/01/1998 to 23/07/2014 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The description of datasets. Figure 7: Root mean squared error (RMSE) of different dimensions of embeddings. (**a**) Experimental results on MovieTweetings-latest dataset; (**b**) Experimental results on DigitalMusic dataset. contain the latent information about the requirement of optimal rating-prediction, so that the more similar the neighbor, the greater the contribution to the rating prediction. Thus, this experiment proved that embedding trained using the rating as the optimization target is able to reduce the number of K-neighbors and hence reduce the computation of the collaborative part. Finally, we chose K = 10 (the minimum K value defined in this paper) as the number of neighbors in the RICF algorithm. Most notably, this paper investigates the effects of different decay functions in the RICF as well as the comparisons between the traditional itemCF and the state-of-art sequential model that deals with temporal information. In addition, to better explore the process of user preference decay with inhibition, we selected four different ways to model the decay of preferences: (1) without RI effects (without-RI), (2) Power function: obviously decay \"quickly then slowly\", (3) Exponential function: no obvious \"quickly then slowly\", (4) Parabolic function: as a contrast, it is clear that decay \"slowly then quickly\". Finally, the main experiment results on the two datasets are seen in Tables 3-5. \begin{table}
\begin{tabular}{c c c} \hline  & **RMSE** & **MAE** \\ \hline without-RI & 0.6711 & 0.4919 \\ Power-RICF & 0.6606 & 0.4850 \\ Exponential-RICF & 0.6663 & 0.4877 \\ Parabolic-RICF & 0.6680 & 0.4895 \\ ItemCF & 0.7741 & 0.5752 \\ Embedding+LSTM & 0.7892 & 0.5933 \\ Embedding+GRU & 0.7908 & 0.5935 \\ \hline \end{tabular}
\end{table} 
Table 4: RMSE of different comparative experiments on the MovieTweetings-100k dataset. Figure 8: RMSE of K-neighbors selection on MovieTweetings-latest dataset. \begin{table}
\begin{tabular}{c c c} \hline  & **RMSE** & **MAE** \\ \hline without-RI & 0.6272 & 0.4624 \\ Power-RICF & 0.6256 & 0.4609 \\ Exponential-RICF & 0.6260 & 0.4614 \\ Parabolic-RICF & 0.6261 & 0.4616 \\ ItemCF & 0.6809 & 0.5134 \\ Embedding+LSTM & 0.8121 & 0.6385 \\ Embedding+GRU & 0.7870 & 0.6158 \\ \hline \end{tabular}
\end{table} 
Table 3: RMSE of different comparative experiments on the MovieTweetings-latest dataset.