A classical problem that frequently compromises Recommender System (RS) accuracy is the sparsity of the data about the interactions of the users with the items to be recommended. The use of side information (e.g. movi...
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A classical problem that frequently compromises Recommender System (RS) accuracy is the sparsity of the data about the interactions of the users with the items to be recommended. The use of side information (e.g. movie domain information) from a Knowledge Graph (KG) has proven effective to circumvent this problem. However, KG growth in terms of size and complexity gives rise to many challenges, including the demand for high-cost algorithms to handle large amounts of partially irrelevant and noisy data. Meanwhile, though Graph Summarization (GS) has become popular to support tasks such as KG visualization and search, it is still relatively unexplored in the KG-based RS domain. In this work, we investigate the potential of GS as a preprocessing step to condense side information in a KG and consequently reduce computational costs of using this information. We propose a GS method that combines embedding based on latent semantics (ComplEx) with nodes clustering (K-Means) in single-view and multi-view approaches for KG summarization, i.e. which act on the whole KG at once or on a separated KG view at a time, respectively. Then, we evaluate the impacts of these alternative GS approaches on several state-of-the-art KG-based RSs, in experiments using the MovieLens 1M dataset and side information gathered from IMDb and DBpedia. Our experimental results show that KG summarization can speed up the recommendation process without significant changes in movie recommendation quality, which vary in accordance with the GS approach, the summarization ratio, and the recommendation method.
Caching state data of real-world entities just in the cloud without any distinction will cause search performance degrading, due to the characteristics of uncountable number of entities and time-varying state of entit...
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Caching state data of real-world entities just in the cloud without any distinction will cause search performance degrading, due to the characteristics of uncountable number of entities and time-varying state of entities in cyber-physical systems (CPSs). Considering the diverse time-varying features of CPS entities, an edge-cloud collaborative entity state data caching strategy toward networking search application in CPSs is proposed in this article. Specifically, an entity state feature extraction method is presented to mine underlying changing rules of CPS entities via raw entity state observation sequence. Then, an edge and cloud collaborative entity state data caching strategy is devised to improve the search accuracy of CPSs search service and reduce the search delay and energy consumption, in which entities are clustered first according to the time-varying degree of their state and then these state information are discriminately cached based on their belonging clusters. Simulation results validate the effectiveness of the proposed strategy in terms of real-time and accuracy performances.
Large scale knowledge graph (KG) has attracted wide attentions in both academia and industry recently. However, due to the complexity of SPARQL syntax and massive volume of real KG, it remains difficult for ordinary u...
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ISBN:
(纸本)9781450368223
Large scale knowledge graph (KG) has attracted wide attentions in both academia and industry recently. However, due to the complexity of SPARQL syntax and massive volume of real KG, it remains difficult for ordinary users to access KG. In this demo, we present VISION-KG, a topic-centric visualization system to help users navigate KG easily via entity summarization and entity clustering. Given a query entity v(0), VISION-KG summarizes the induced subgraph of v(0)'s neighbor nodes via our proposed facts ranking method that measures importance, relatedness and diversity. Moreover, to achieve conciseness, we split the summarized graph into several topic-centric summarized subgraph according to semantic and structural similarities among entities. We will demonstrate how VISION-KG provides a user-friendly visualization interface for navigating KG.
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