# TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning
## 5 Experiments
### Baselines and Parameter Settings  
To show the effectiveness of the proposed TRAL, we compare the recommendation performance with the following baselines.  
* CCF: CCF uses hierarchical clustering to obtain different tag clusters, each of which can be viewed as the representation of a certain topic area [3]. Cluster-based feature vectors for users and items are generated and the relevance relation between them can be estimated.
* ACF: ACF introduces the deep autoencoders to derive tag-based user latent features, on which user-based CF is performed to provide recommendations [5].
* NCF: It is a general framework to employ neural network architectures for CF [11]. By replacing the inner product with a MLP, NCF can learn non-linear interactions between users and items.
* DSPR: DSPR adopts MLPs with shared parameters to extract latent features for users and items based on tagging information [24]. Deep-semantic similarities between target users and their relative items can be computed to generate the ranked recommendation list.
* TRSDL: It is a tag-aware recommendation method, which introduces pre-trained word embeddings to represent tag information and learns latent features of users and items via deep structures [10].
* BPR-T: It is a ranking-based collaborative filtering model which incorporates the tag mapping scheme and the Bayesian ranking optimization [20].
* STEM: STEM establishes a new social tag expansion model to tackle the problem of tag sparsity, thereby improving the recommendation accuracy [22].  
To guarantee the recommendation performance, we randomly select one interaction for each user as the validate set to determine hyper-parameters of each model. For the sake of fairness, each model is optimized by the mini-batch Adam [38]. The learning rate of each model is tuned in [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05], while the batch size is searched from [128, 256, 512, 1024]. The number of negative samples is search from 1 to 10. The maximum number of iterations for optimization is fixed to 300 for all the models. Early stopping strategy is also applied in the light of the performance on the validation set. For the proposed TRAL, the embedding dimension of tags and the attention factor are set as 32 and 16, respectively. The embedding dimension of tag frequency is fixed to 8. In addition, we construct a tower network architecture with three layers. The c is searched from [\(1\times 10^{-5},1\times 10^{-4},1\times 10^{-3},1\times 10^{-2},1\times 10^{- 1}\)]. The specific parameters of our model are listed in Table 2. The proposed model is implemented with Pytorch and all experiments are conducted on a PC configured with an Intel Core I9-10900X @3.40 GHz CPU with 32 GB memory, and an Nvidia GeForce RTX 3080 Ti GPU with 12 GB memory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & embedding size of tags & 32 \\ MLP and Attention Learning & embedding size of tag & 8 \\  & frequency & 8 \\  & size of hidden layers & [80; 40; 20] \\  & attention factor & 16 \\ \hline  & optimizer & Adam \\  & learning rate & 0.001 \\ Training Process & maximum number & 300 \\  & of iterations & 256 \\  & batch size & 256 \\  & regularization & \(L_{2}\) norm \\  & number of negative samples & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific parameters of the proposed TRAL used in experiment.