# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 5 Experiments
### Experimental Results Analysis  
In this subsection, we compare the proposed TMRec model with several other methods for top-\(N\) recommendation. The comparison models can be divided into two categories: CF-based models, including ItemKNN, BPR, MF, and NCF, and HIN-based models, including the meta-paths HAN, MCRec, AMERec, and MEGNN. 1. ItemKNN [2]: It is a classical method of collaborative filtering using previous items as the reference. For example, if one wants to predict \(u_{1}\)'s rating on movie \(m_{1}\), one first needs to keep a list of \(k\) movies that \(u_{1}\) has watched and evaluated and then give a prediction of the rating of \(m_{1}\) by \(u_{1}\) according to the historical ratings. 2. BPR [3]: It is a commonly used recommendation model in current recommender systems. Unlike other methods based on a user scoring matrix, it mainly adopts the users' implicit feedback (such as clicks and favorites) to sort items through the maximum posterior probability obtained by the Bayesian analysis of problems and then generates recommendations. 3. MF [24]: It is proposed to solve the shortcomings of CF, which has a weak ability to process sparse matrices. It optimizes the MF model with the cross-entropy loss. 4. NCF [25]: It is a variant of the traditional collaborative filtering model. Since the previous inner product of collaborative filtering is too simple to perform well, it utilizes an MLP to replace this operation, combining neural networks and collaborative filtering. 5. HAN [13]: It adopts a hierarchical structure of the attention mechanism, including node-level and semantic-level attention. The former is used to distinguish the importance of nodes in a meta-path, while the latter is utilized to learn how much each meta-path contributes to the generation of the final embedding. 6. MCRec [16]: It is the first method to explicitly express meta-paths, which are combined with the user and item embeddings for recommendation. It also introduces the co-attention mechanism to mutually enhance the representations of meta-paths, users, and items. 7. GraFC2T2 [17]: It is a general graph-based framework that combines some side information for top-N recommendation. It encodes content-based features, and temporal and trust information into a complex graph and uses personalized PageRank on this graph to provide recommendations. 8. MEGNN [44]: It is capable of discovering and extracting the most expressive meta-paths and avoids manually defining multiple meta-paths. It also uses a heterogeneous convolution module to generate trainable heterogeneous graph structures. The results of the proposed model and baselines on three datasets are shown in Table 2. The major findings from Table 2 are reported below. 1. The proposed TMRec consistently outperforms all baselines on the three datasets. The results show the effectiveness of TMRec in top-\(N\) recommendation tasks; the model considers the temporal relations of nodes of different kinds and the mutual influence of nodes of the same type in user and item embedding learning. 2. It can be clearly seen that when comparing the two different types of methods, HIN-based methods perform better than CF-based methods in general. As HIN-based methods can catch richer semantic information, they are particularly useful in addressing cold-start problems in recommendation. Therefore, they have been more widely used in recommendation tasks, especially when there are many different types of nodes and links in a dataset. 3. TMRec outperforms HAN, MCRec, and MEGNN among the HIN-based methods. HAN utilizes a hierarchical attention mechanism at the node and semantic levels, while MCRec employs a co-attention mechanism for the user, item, and meta-path embeddings. In addition, MEGNN considers the selection of meta-paths without considering the explicit expression of the meta-paths. In contrast, the proposed TMRec model uses co-attention in meta-path embedding learning and self-attention in user and item embedding learning.