A location-based orientation-aware recommender system using IoT smart devices and Social Networks

2020 ◽  
Vol 108 ◽  
pp. 97-118 ◽  
Author(s):  
Soroush Ojagh ◽  
Mohammad Reza Malek ◽  
Sara Saeedi ◽  
Steve Liang
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
Smart Devices

scholarly journals Location-based Orientation Context Dependent Recommender System for Users

2021 ◽  
Vol 3 (1) ◽  
pp. 14-23
Author(s):  
Vijesh Joe C ◽  
Jennifer S. Raj
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
User Preferences ◽  
Smart Phones ◽  
Smart Devices ◽  
Location Information ◽  
Efficient Manner ◽  
Recommender Algorithm ◽  
Almost All ◽  
Ultimate Purpose

As the technology revolving around IoT sensors develops in a rapid manner, the subsequent social networks that are essential for the growth of the system will be utilized as a means to filter the objects that are preferred by the consumers. The ultimate purpose of the system is to give the customers personalized recommendations based on their preference. Similarly, the location and orientation will also play a crucial role in identifying the preference of the customer is a more efficient manner. Almost all social networks make use of location information to provide better services to the users based on the research performed. Hence there is a need for developing a recommender system that is dependent on location. In this paper, we have incorporated a recommender system that makes use of recommender algorithm that is personalized to take into consideration the context of the user. The preference of the user is analysed with the help of IoT smart devices like the smart watches, Google home, smart phones, ipads etc. The user preferences are obtained from these devices and will enable the recommender system to gauge the best resources. The results based on evaluation are compared with that of the content-based recommender algorithm and collaborative filtering to enable the recommendation engine’s power.

SOS: A multimedia recommender System for Online Social networks

2019 ◽  
Vol 93 ◽  
pp. 914-923 ◽  
Author(s):  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Francesco Piccialli
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
Online Social Networks

Social Internet of Things in Healthcare

2017 ◽  
pp. 88-111 ◽  
Author(s):  
Cristina Elena Turcu ◽  
Corneliu Octavian Turcu
Keyword(s):  
Social Networks ◽  
Internet Of Things ◽  
Agent Technology ◽  
Smart Devices ◽  
Collaborative Environments ◽  
The Social ◽  
Social Internet Of Things ◽  
New Applications ◽  
Future Vision

This chapter presents a future vision for healthcare, which will involve smart devices, Internet of Things, and social networks, that make this vision a reality. The authors present the necessary background by introducing the Social Internet of Things paradigm. Agent technology seems to be a promising approach in the adoption of the Social Internet of Things in collaborative environments with increased autonomy and agility, like healthcare is. Also, it is examined challenges to the adoption of the Social Internet of Things in healthcare in order to facilitate new applications and services in more effective and efficient ways.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

Sign in / Sign up

Export Citation Format

Share Document