Existing research on Temporal Knowledge Graph (TKG) representation learning focuses on decomposing events into entities and relations, then employing various approaches to learn entity and relation representations in a low-dimensional vector space. However, all existing research overlooks the correlations among events, even though events are the core constituent elements of TKGs and involve heterogeneous correlations. To this end, we propose a Heterogeneous evolutionary Event cluster Aware Representation learning approach in Temporal Knowledge Graphs (HEART), which is the first event-centric approach in TKGs. Specifically, a heterogeneous event graph construction module is proposed to capture the diverse pairwise correlations between events by building co-entity and proximity heterogeneous edges. In addition, an event-aware multi-step evolutionary clustering module is proposed to capture continuous high-order correlations among events at different timestamps. Furthermore, an event cluster-aware unsupervised alignment mechanism is proposed to preserve the temporal smoothness of event clusters through cross-temporal alignment. Moreover, an event cluster-based self-supervised optimization mechanism is proposed to optimize the representations of event clusters. Experimental results on seven real-world datasets demonstrate that HEART achieves the state-of-the-art performance, outperforming the runner-up by an average of 3.89%, 8.14%, 5.97%, and 6.92% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

Feature interaction modeling, which exploits interactive information between features, has been widely explored in various applications. Recently, many graph or hypergraph structures-based models have been proposed to model feature interactions by predicting the existence of edges or hyperedges among nodes. However, these models lack the capability to capture the inherent comparability among samples, where multiple samples exhibit both shared and distinct characteristics, and such comparable relationships are often beneficial for prediction. To this end, we propose SR-HyperFM, Sample Relationship aware Hypergraph Factorization Machines, which incorporate sample comparable relationships into feature interaction modeling, leveraging both shared features and critical differences among samples. Specifically, the sample relationship aware hypergraph construction module is introduced to fully capture the comparable relationships among samples and discover beneficial high-order feature interactions. In addition, the dual hypergraph message passing module explicitly models feature interactions by exploiting these inherent relationships. Extensive experiments on four real-world datasets demonstrate the superiority of SR-HyperFM. In addition, case studies are conducted to further justify the effectiveness of SR-HyperFM.

Predicting user click behavior and making relevant recommendations based on the user’s historical click behavior are critical to simplifying operations and improving user experience. Modeling User Interface (UI) elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3 664 325 clicks of 100 users and propose a UI elementSpatialHierarchyAwareSmartphone userClick behaviorPrediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 18.35$\%$, 13.86$\%$, and 11.97$\%$in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively.

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a **D**eep **E**volutionary **C**lustering jointed temporal knowledge graph **R**epresentation **L**earning approach (**DECRL**). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9.53\%, 12.98\%, 10.42\%, and 14.68\% in MRR, Hits@1, Hits@3, and Hits@10, respectively.