Service Recommendation: Similarity-Based Representative Skyline

2010 ◽  
Author(s):  
Liang Chen ◽  
Jian Wu ◽  
Shuiguang Deng ◽  
Ying Li
Keyword(s):  
Service Recommendation ◽  
Representative Skyline

scholarly journals Web service recommendation based on event ontology

2021 ◽  
Vol 1955 (1) ◽  
pp. 012054
Author(s):  
Hang Lv ◽  
Junjun Pan ◽  
Jinliang Wu ◽  
Haiyang Ren
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Event Ontology

Discovering the Representative Skyline Over a Sliding Window

2016 ◽  
Vol 28 (8) ◽  
pp. 2041-2056 ◽  
Author(s):  
Mei Bai ◽  
Junchang Xin ◽  
Guoren Wang ◽  
Luming Zhang ◽  
Roger Zimmermann ◽  
...  
Keyword(s):  
Sliding Window ◽  
Representative Skyline

scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

scholarly journals Graph-based service recommendation in Social Internet of Things

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110090
Author(s):  
Yuanyi Chen ◽  
Yanyun Tao ◽  
Zengwei Zheng ◽  
Dan Chen
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Latent Variable ◽  
Smart Cities ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Variable Model ◽  
Completion Problem ◽  
Social Internet Of Things ◽  
Object Usage

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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