Temporal recommendation is an important class of tasks in recommender systems, which focuses on modeling and capturing temporal patterns in user behavior to achieve finer-grained and higher-quality recommendations. In real-world scenario, users' temporal behaviors are not only characterized by sequential dependencies among consecutive items, but also by periodic correlations of different items and time-varying similarity of different users. In this paper, we propose an Adaptive Temporal Recommendation (AdaTR) algorithm to capture the inherent features of temporal behaviors and dynamic collaborative signals. Firstly, based on the periodic characteristics of user behaviors, the user-item interactions are counted and aggregated in different time segments across multiple periods, which forms the temporal user-item interaction matrix. Then, in order to capture the time-varying collaborative signals between different users, a deep spectral clustering (DSC) method is implemented on the temporal user-item interaction matrix, where the original representation of user-item interaction is projected into a latent space, and users' temporal behaviors are clustered into different groups. Furthermore, an Adaptive Deep Matrix Factorization (AdaDMF) module is designed to learn the time-varying representations of user preferences on each cluster of temporal user behaviors, which incoporate dynamic collaborative signals among different users. Finally, we combine users' short-term and long-term preferences to generate personalized temporal recommendations. Extensive experiments on four datasets demonstrate that AdaTR performs significantly better than the state-of-the-art baselines.

Mining temporal patterns from user behaviors has long been investigated, but most of the existing work centers on single-type user–item interactions, such as purchase or click, which fails to take advantage of the user’s diversified interests revealed by various types of behavior. However, capturing patterns from different behavior sequences and modeling the complex inter-correlation between them are non-trivial tasks, as the high sparsity of type-related interactions, multi-seasonality of individual behaviors, and time-variant dependency of multi-type activities make it really challenging. To address these challenges, we propose a novel framework that aims to model the M ulti-Seasonal M ulti-Behavior Dep endencies (MMDep) both within and across the multi-type behavior sequences. In the proposed model, an item co-occurrence matrix factorization strategy is introduced to alleviate the sparsity issue in type-related behavior sequences. And a temporal dependency module that incorporates multi-scale EMA mechanism is utilized to capture the multi-seasonal dependencies within individual sequences. Moreover, a cross-behavior dependency module is employed to learn the time-variant dependency among different behaviors. Extensive experiments on three real-world datasets demonstrate that the proposed MMDep performs significantly better than the state-of-the-art baselines. And it may provide some new insights and tools on how to leverage multi-behavior data for better temporal recommendation.

Abstract Link prediction across different knowledge graphs (i.e. Cross-KG link prediction) plays an important role in discovering new triples and fusing multi-source knowledge. Existing cross-KG link prediction methods mainly rely on entity and relation alignment, and are challenged by the problems of KG incompleteness, semantic implicitness and ambiguosness. To deal with these challenges, we propose a learning framework that incorporates both node-level and substructure-level context for cross-KG link prediction. The proposed method mainly consists of a neural-based tensor-completion module and a graph-convolutional-network module, which respectively captures the node-level and substructure-level semantics to enhance the performance of cross-KG link prediction. Extensive experiments are conducted on three benchmark datasets. The results show that our method significantly outperforms the state-of-the-art baselines and some interesting analysis on real cases are also provided in this paper.

User activities in real systems are usually time-sensitive. But, most of the existing sequential models in recommender systems neglect the time-related signals. In this article, we find that users' temporal behaviors tend to be driven by their regularly changing states, which provides a new perspective on learning users' dynamic preference. However, since the individual state is usually latent, the event space is high dimensional, and meanwhile, temporal dependency of states is personalized and complex; it is challenging to represent, model, and learn the time-evolving patterns of user's state. Focusing on these challenges, we propose a deep structured state learning (DSSL) framework, which is able to learn the representation of temporal states and the complex state dependency for time-sensitive recommendation. Extensive experiments demonstrate that the DSSL achieves competitive results on four real-world recommendation datasets. Furthermore, experiments also show some interesting rules for designing the state dependency network.

HPC2-ARS supports a high performance cloud computing (HPC2) based streaming data analytic system, which ensures real-time response on unpredictable and fluctuating Big Data Streams by provisioning and scheduling computing resources autonomously. It focuses on parallel high-volume streaming applications, which have stringent real-time constraints and bring Big Data issues. It is a brand-new three-layered architecture, which solves three essential problems: (a) how many resources are needed for each application to achieve real-time analytic on streaming Big Data, (b) where to best place the allocated resources to minimize resource consumption, and (c) how to minimize response time for parallel applications. In summary, HPC2-ARS provides high performance streaming services.

Gene selection is to select the most informative genes from the whole gene set. It's an important preprocessing procedure for the discriminant analysis of microarray data, because many of the genes are irrelevant or redundant to the discriminant problem. In this paper, the gene selection problem is considered as a gene ranking problem and a random subspace method based gene ranking (RSM-GR) algorithm is proposed. In RSM-GR, firstly subsets of the genes are randomly generated; then Support Vector Machines are respectively trained on each subset and thus produce the importance factor of each gene; finally, the importance of each gene obtained from these randomly selected subsets is combined to constitute its final importance. Experiments on two public datasets show that RSM-GR obtains gene sets leading to more accurate classification results than other gene selection methods, and it demands less computational time. RSM-GR can also better deal with datasets with a large number of genes and a big number of genes to be selected.