# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 5 Experiments
### Item Embedding Visualization (RQ4)  
To evaluate whether the proposed HKRec can better distill implicit semantics from the KG and strengthen item representations, we conduct visual experiments on item similarity within the MIND news recommendation dataset by extracting embeddings of diverse item topics and subsequently computing a similarity matrix. Specifically, three items are selected from each of the topics (i.e., actor, institution, and sports). We calculate the cosine similarity between items, where the similarity value range is \([-1,1]\). The visual comparison results between our mode and KGCL are illustrated in Figure 6. Briefly, the darker the color, the lower the similarity between items, and the lighter the color, the higher the similarity. From the results, we summarize the following observations:  
* We find that the lightest colors are observed among items of the same topic and the highest similarity between items of the same topic. Specifically, Figure 5(a) presents the experimental result of HKRec, showcasing a similarity range of [0.82, 1] for actor items, [0.95, 1] for institutions, and [0.79, 1] for sport items. This is because in recommender systems, items of the same topic typically share similar features, exhibit similar contexts in the KG, and produce simultaneous interactions in the UIG. Yet, as illustrated in Figure 5(b), when employing KGCL, the similarity ranges are [0.45, 1], [0.20, 1], and [0.58, 1] separately. Overall, HKRec effectively learns the correlations among items of the same topic, enabling their close positions to each other in the embedding space and performing high-quality item embeddings. However, KGCL exhibits limitations in its node representation learning ability, especially for sports-related items. Figure 4: Comparison results in different user groups. The lower group number indicates the scarcity of historical interaction information, leading to a more pronounced cold-start effect. Figure 5: Comparison results in different item sparsity levels. * Compared to items of the same topic, the visualization matrix colors between items of different topics are darker, indicating lower similarity. Specifically, for institutional items, the colors of the similarity matrix between institutions and actors and sports items are the darkest. This is attributed to the fact that in UIG, only one or two users simultaneously click on both institutional and actor items or both institutional and sports items. And there is no correlation between institutions and the other two types of items in KG. * There is similarity between actors and sports, ranging from 0.55 to 0.81. This phenomenon arises from users clicking on both types of items concurrently in the UIG, while the correlation between these two categories in the KG further contributes to the observed distinctions in similarities. For example, in the case of HKRec, the similarity values between the actor _[PERSON]_ and the sports of _World Series_ and _Pittsburgh Steelers_ are measured at 0.79 and 0.61, respectively. As shown in Figure 7, the rationale behind these findings lies in the fact that almost 30 users concurrently click news related to _[PERSON]_ and _World Series_, as well as _[PERSON]_ and _Pittsburgh Steelers_. This limited interaction results in a weaker correlation between _[PERSON]_ and _World Series_, as well as between _[PERSON]_ and _Pittsburgh Steelers_. However, the correlation relationship between these two sets of items in the KG provides useful information to the user and makes the correlation between these two sets of items stronger. Specifically, as shown in Figure 7a, the analysis of KG reveals that, in comparison to _Pittsburgh Steelers_, there is one three-hop path between _[PERSON]_ and _World Series_, while no three-hop path exists between _[PERSON]_ and _Pittsburgh Steelers_. Additionally, as shown in Figure 7b, there are twenty-six four-hop paths between _[PERSON]_ and _World Series_, whereas only eleven paths exist between _[PERSON]_ and _Pittsburgh Steelers_.