Smart object recommendation (SORec) architecture using representation learning in Smart objects-Based Social Network (SBSN)

2021 ◽  
Author(s):  
Pratibha Mahajan ◽  
Pankaj Deep Kaur
Keyword(s):  
Social Network ◽  
Representation Learning ◽  
Smart Objects ◽  
Smart Object

A Localized and Efficient System for Smart Shopping Using Internet of Things

2019 ◽  
Vol 16 (8) ◽  
pp. 3201-3203 ◽  
Author(s):  
K. Ashokkumar ◽  
K. Amirtha ◽  
G. Akshaya
Keyword(s):  
Performance Evaluation ◽  
Internet Of Things ◽  
The Internet ◽  
Price Difference ◽  
Smart Objects ◽  
Efficient System ◽  
Smart Object ◽  
Rfid Tag

A smart object can be any object that is connected to the internet and its used for any specific use. Numerous applications can be performed by making use of these smart objects. IoT is used in various applications and has obtained an immense growth in this technical era. Use of IoT in shopping can be of great use. Various research works have been propped to design various applications that could be used for smart shopping. In this paper, an RFID tag is attached to each product which consists of the information about the product. When a user tends to shop for a particular product he enters his wish list in the application provided in his smartphone. The application gives him the shops where the product is readily available and also gives the entire comparison of the product in all the possible shops. Some of the comparisons that are specified are the price difference, the location of the shop, the timings when the shop will be open and lots more. The user can compare all the possible comparisons and decide which shop he can buy the product. The performance evaluation of the application is done and numerous parameters are a measure to make it an efficient smart shopping system.

scholarly journals Enhancing the navigability in a social network of smart objects: A Shapley-value based approach

2016 ◽  
Vol 103 ◽  
pp. 1-14 ◽  
Author(s):  
L. Militano ◽  
M. Nitti ◽  
L. Atzori ◽  
A. Iera
Keyword(s):  
Social Network ◽  
Shapley Value ◽  
Smart Objects

scholarly journals Inferring Users’ Social Roles with a Multi-Level Graph Neural Network Model

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1453
Author(s):  
Chunrui Zhang ◽  
Shen Wang ◽  
Dechen Zhan ◽  
Mingyong Yin ◽  
Fang Lou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Middle Managers ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Government Officials ◽  
Senior Managers ◽  
Accuracy Improvement ◽  
Average Accuracy ◽  
Multi Level

Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.

scholarly journals Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network

2020 ◽  
Author(s):  
Bonaventure C. Molokwu ◽  
Ziad Kobti
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Social Graph ◽  
Wide Range ◽  
Social Graphs ◽  
Prediction Problems

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.

Heterogeneous graph-based joint representation learning for users and POIs in location-based social network

2020 ◽  
Vol 57 (2) ◽  
pp. 102151 ◽  
Author(s):  
Yaqiong Qiao ◽  
Xiangyang Luo ◽  
Chenliang Li ◽  
Hechan Tian ◽  
Jiangtao Ma
Keyword(s):  
Social Network ◽  
Representation Learning ◽  
Location Based Social Network ◽  
Joint Representation

scholarly journals Empowering Museum Experiences Applying Gamification Techniques Based on Linked Data and Smart Objects

2020 ◽  
Vol 10 (16) ◽  
pp. 5419
Author(s):  
Alejandro López-Martínez ◽  
Álvaro Carrera ◽  
Carlos A. Iglesias
Keyword(s):  
Internet Of Things ◽  
Cultural Heritage ◽  
Linked Data ◽  
Real Life ◽  
Smart Objects ◽  
Semantic Web Technologies ◽  
Smart Object ◽  
Web Technologies ◽  
Interactive Question ◽  
Advanced Interaction

Museums play a crucial role in preserving cultural heritage. However, the forms in which they display cultural heritage might not be the most effective at piquing visitors’ interest. Therefore, museums tend to integrate different technologies that aim to create engaging and memorable experiences. In this context, the emerging Internet of Things (IoT) technology results particularly promising due to the possibility of implementing smart objects in museums, granting exhibits advanced interaction capabilities. Gamification techniques are also a powerful technique to draw visitors’ attention. These often rely on interactive question-based games. A drawback of such games is that questions must be periodically regenerated, and this is a time-consuming task. To confront these challenges, this paper proposes a low-maintenance gamified smart object platform that automates the creation of questions by exploiting semantic web technologies. The platform has been implemented in a real-life scenario. The results obtained encourage the use of the platform in the museum considered. Therefore, it appears to be a promising work that could be extrapolated and adapted to other kinds of museums or cultural heritage institutions.

Sign in / Sign up

Export Citation Format

Share Document