Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop endto-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity.In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-ofthe-art methods like KGAT [41], and CKAN [47]. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/ huangtinglin/Knowledge_Graph_based_Intent_Network.
A novel deadline assignment strategy for a large batch of parallel tasks with soft deadlines in the cloud.
Workflow temporal verification is conducted to guarantee on-time completion, which is one of the most important QoS (Quality of Service) dimensions for business processes running in the cloud. However, as today's business systems often need to handle a large number of concurrent customer requests, conventional response-time based process monitoring strategies conducted in a one-by-one fashion cannot be applied efficiently to a large batch of parallel processes because of significant time overhead. Similar situations may also exist in software companies where multiple software projects are carried out at the same time by software developers. To address such a problem, based on a novel runtime throughput consistency model, this paper proposes a QoS-aware throughput based checkpoint selection strategy, which can dynamically select a small number of checkpoints along the system timeline to facilitate the temporal verification of throughput constraints and achieve the target on-time completion rate. Experimental results demonstrate that our strategy can achieve the best efficiency and effectiveness compared with the state-ofthe-art as and other representative response-time based checkpoint selection strategies. 2 weeks [1,2]. Failures of completing these processes in time will result in significant deterioration of customer satisfaction and even huge financial losses. Therefore, on-time completion becomes one of the most important QoS dimensions that pervade the design, development, and running of business process management systems, for example, the cloud workflow system [2]. In the meantime, cloud computing is establishing itself as a new paradigm for delivering information technology (IT) infrastructure elements such as computing, storage and A general workflow temporal QoS framework was proposed in [12], which can deliver a lifecycle QoS support. In recent years, many checkpoint selection strategies, from intuitive rule based to sophisticated model based, have been proposed. The work in [22] takes every workflow activity as a checkpoint. The work in [23] selects the start activity as a checkpoint and adds a new checkpoint after 288 XIAO LIU ET AL. 1) Given the runtime throughput consistency model, determine the current temporal consistency state α %;2) If α %≥ β % then break; Else, report a detected potential temporal violation. addition, because the number of selected checkpoints determines the number of possible violation handling, which dominates the cost of the entire temporal QoS framework, as in many studies [12,13,26], the efficiency of temporal consistency monitoring should also consider the number of selected checkpoints.According to our experiments, the computation overhead of our strategy is very small (in milliseconds) and linear to the number of workflow activities. Considering most workflow activities are running in seconds or minutes, the computation overhead is trivial and, thus, can be neglected. This is consistent to the results of the previous work [15]. In contrast, the major time overhead is p...
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