# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 5 Experiments
### Performance Comparison  
Table 2 presents the performance comparison of the proposed DTGCF and other compared methods on two datasets in terms of accuracy, diversity, and trade-off metrics. From the results, we can have several findings:  
1. For the tag-aware recommendation models, we can observe that CCF and ACF consistently perform worse than other models based on deep learning, such as DSPR, TGCN, and LFGCF, revealing the superiority of deep neural networks on representation learning adequately. Although introducing the deep autoencodes, ACF do not construct the direct correlation between the deep learning model and the recommendation performance, which limits the model expression ability. Hence, the resulting user representation may not be accurate enough for providing high-quality recommendations;
2. GCN-based tag-aware models, including TGCN, LFGCF, and DTGCF, outperform other models for a large margin in terms of accuracy, verifying the importance of integrating rich semantic information from neighbors. In addition, the results of LFGCF and DTGCF are significantly better than that of TGCN, which demonstrates the effectiveness and robustness of the simplified architecture;
3. In terms of diversity, the results of MMR and DPP are obviously better than those of accuracy-focused models such as CCF, ACF, TGCN, and LFGCF. It can be found that the recommendation accuracy significantly degrades so as to keep a good diversity. In particular, DGCN performs slightly worse than TGCN in terms of accuracy but achieves significantly better diversity, indicating the rationality of the end-to-end diversified recommendation model;
4. The proposed DTGCF achieves better comprehensive performance than all the baselines according to the trade-off metric (F1). The relative improvement of DTGCF over the best competitors on two datasets are 7.40%, and 5.98% on F1@10, and 5.62%, and 5.53% on F1@20, respectively. In terms of accuracy, the proposed model ranks first in most cases, except for Recall@20 on the 10M dataset, and Recall@10 on the 20M dataset. In addition, DTGCF also outperforms diversity-promoted methods in most cases in terms of diversity. Although performing slightly worse than DPP on 20M dataset in ILD@20, our model has substantially better performance in terms of accuracy. Compared to DGCN, DTGCF generates better results in both accuracy and diversity on two datasets, which demonstrates the effectiveness of our diversified tag-aware model.  
To summarize, the proposed DTCCF can gain superior or competitive performance in the light of the recommendation accuracy while greatly promoting the diversity. We attribute the advantages of our model to the following three aspects. Firstly, a simplified GCN customized for tag-aware recommendations is conducted to ensure a considerable level of recommendation accuracy. Secondly, we leverage the personalized category-boosted negative sampling and adversarial learning to substantially improve the recommendation diversity. Thirdly, the two-way TransTag regularization is performed to properly model the three-dimensional relation among users, tags, and items, which can further enhance the ability of feature expression and generate more satisfactory recommendations.