DUSKG: A fine-grained knowledge graph for effective personalized service recommendation

Wang, Haifang; Wang, Zhongjie; Hu, Sihang; Xu, Xiaofei; Chen, Jinjun; Tu, Zhiying

doi:10.1016/j.future.2019.05.045

Cited by 34 publications

(19 citation statements)

References 20 publications

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“…Khelloufi et al 30 proposed a service recommendation system by considering the social relationships between device owners, as incorporating user's social features can enable contextaware recommendation and increase the efficiency of the recommended services. To fuse multiple types of service data and their logical relations for personalized service recommendation methods, Wang et al 31 proposed domain-oriented user and service interaction knowledge graph. As privacy-preserving schemes are very important for service recommendation in SIoT, Ferrag et al 32 surveyed recent research literatures on research trends in privacy-preserving schemes for ad hoc social networks.…”

Section: Related Workmentioning

confidence: 99%

Graph-based service recommendation in Social Internet of Things

Chen

Tao

Zheng

et al. 2021

International Journal of Distributed Sensor Networks

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While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

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Section: Related Workmentioning

confidence: 99%

Graph-based service recommendation in Social Internet of Things

Chen

Tao

Zheng

et al. 2021

International Journal of Distributed Sensor Networks

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“…An efficient service discovery mechanism, therefore, must satisfy a user service request on the fly by returning only context-tailored or relevant services. To satisfy this requirement existing solutions have mainly adopted what can be described as a "user-centric" approach: adapting services requests to return relevant services based on the knowledge about the user's profile [23] which includes user preferences [9], current activities, physical location, and surrounding objects [24]. However, Badidi [10] has argued in support of the need for a more comprehensive approach towards service personalization.…”

Section: A Case For Resource-awarenessmentioning

confidence: 99%

A Resource-aware Service Discovery Architecture for Ad-hoc Mobile Cloud

Egbe¹

2020

IJETER

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The synergy between Mobile Cloud Computing and the proliferation of smart devices is increasingly expanding the interaction space for mobile web services delivery. More so, with the rising prominence of the IoT paradigm, which seeks to drive a smarter and unlimited interconnected interaction between diverse devices, the need for opportunistic and inexpensive service provisioning platforms becomes more imperative. Such platforms stand to potentially contribute toward realizing the Ad-hoc Mobile Cloud model which advocates the idea of mobile web services (MWS): enabling mobile and other smart devices to function as service providers in addition to their conventional role as service requesters. However, mobile, and smart devices are inherently characterized by limitations in resources such as battery power, memory, and storage capacities. Consequently, executing service discovery operation on the local device can introduce a huge resource burden. Therefore, to achieve service discovery in such a decentralized mobile device-enabled e-services provisioning platform without compromising the devices' resources requires an architecture designed to support capabilities for proactive resource management. In this paper, we propose an architecture that supports service discovery in Ad-hoc Mobile Cloud (AMC) through a mechanism driven by resource-awareness.

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“…The proposed approach was executed on some real-world dataset and the performance was evaluated for knowledge completion, search, ranking and recommendation processes [10]. Another approach that introduced a fine-grained knowledge graph for providing better personalized recommendations called DUSKG [11] was another approach that attempted incorporating multiple types of service data considering their logical relations. The authors proposed a compact data representation model incorporating different sorts of logical relations between the data.…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

“…The authors proposed a compact data representation model incorporating different sorts of logical relations between the data. The five major relationships such as "FocusOn", "BelongTo, "USimilar", "SSimilar" and "FSimilar" are considered for their experiment and it showed that their proposed approach showed better recommendation performance with least computation time [11].…”

Section: Knowledge Graph-based Recommendation Systemsmentioning

confidence: 99%

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

Sajisha

Anoop

Ansal³

2019

IJMLNCE

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he unprecedented growth of unstructured data poses many challenges in semantic computing, which is an active research area for many years. While unearthing interesting patterns such as entities, relationships, and other metadata are important, it is equally important to represent them in an efficient, easy to access manner. Knowledge Graphs (KGs) are one such mechanism to represent facts extracted from unstructured text. KGs represent entities as nodes and relationships as edges. Such a representation may find applications in many meaning-aware computing applications such as question answering, summarization, etc., to name a few. Very recently, knowledge graph-based recommendation systems have become popular which has many advantages over traditional recommendation engines. This survey is an attempt to summarize and critically evaluate some of the very recent approaches to knowledge graph-based recommendation approaches.

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DUSKG: A fine-grained knowledge graph for effective personalized service recommendation

Cited by 34 publications

References 20 publications

Graph-based service recommendation in Social Internet of Things

Graph-based service recommendation in Social Internet of Things

A Resource-aware Service Discovery Architecture for Ad-hoc Mobile Cloud

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

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