# Sequential Recommendations on GitHub Repository
## 4 Experiments
### Performance Analysis  
In this experiment, we have set the number of \(\mathcal{L}\) as 5 and the number of \(\mathcal{T}\) as 3, which heuristically gave the best performance in training all given models. That is, the input number of each item sequence is 5, predicting the next 3 items in a sequence for each user. The numbers of two cold-start datasets are set to 40 and 200, under the assumption that similar data sparsity of a dataset will result in a similar result. The results of the recommendation algorithms described previously are shown in Table 4. The CNN-based model surpassed all the models in all matrices. Generally, self-attention-based recommendation systems such as SASRec, BERT4 Rec, and AttRec [39] better predict the subsequent items in relatively sparse datasets, especially when CNN-based recommenders have a limited receptive field [11] in regards to the size of the dataset. These results show the presence of being more weighted on the short-term sequential interactions in GitHub or similar platforms. Nonetheless, recommendations based on the RNN, such as GRU4 Rec, model performed worse than the other algorithms. This is particularly because the model learns the sequence in a step-by-step manner, which suggests that too much concentration in the sequence will degrade the overall performance. Consequently, it is necessary to capture dependencies effectively, not only in the short-term but also in long-term information (e.g., users' general preference) [8; 9; 11; 43]. Considering the different \(n\) number of cold-start interactions, Caser outperformed the performance indicators for Pres@10 and MAP in more than 200 interactions. The metrics were extremely close to each other when number of cold start is 40 interactions (n = 40), but the gap wasenlarged because they tend to focus more on users with larger number of interactions. It clearly demonstrates that the CNN-based model, Caser, is robustly capable of capturing short-term sequential interactions, as well as accurately modeling long-term preferences especially in the less receptive areas. Figure 7 presents a predicted output of the predicted repositories from the randomly sampled users in the validation dataset. The five sequential interactions are the input items of the model; and we presented the output predicted items. In this figure, 'category' denotes the hand-crafted feature from each item and the 'language' feature is obtained from each output repository. We manually checked the task or project of the output repositories, and categorized the topic to visualize the example. As the repository was predicted from time step \(t\), it was found that the previous time step \(t-1\) item not only affected the forecast next item but also the next nearby time steps in the sequential recommender. The highest-ranked \(item_{t,r}\) includes a feature ('category') of the immediate previous item and the one prior('language') inclusively. However, \(item_{t,r-1}\) item is rather much closer to \(item_{t-3}\) and \(item_{t-4}\) than the sequentially nearby item, such as second previously interacted item. This reveals that the outputs include various features of sequence perspective and that the recommender can represent users' sequential and general preferences. Figure 7 briefly shows the notion of deep learning-based sequential recommendation and the correlation of typical class features ('type' and 'category') in GitHub recommendation. To sum up, the GitHub platform has an exclusive differentiation between various platforms in recommendation tasks.