CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices

2009 ◽  
Author(s):  
Lakshmish Ramaswamy ◽  
P. Deepak ◽  
Ramana Polavarapu ◽  
Kutila Gunasekera ◽  
Dinesh Garg ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Recommender System ◽  
Context Aware ◽  
Social Recommender System

Context-Aware Recommender System for Location-Based Advertising

2011 ◽  
Vol 467-469 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hyun Chul Ahn ◽  
Kyoung Jae Kim
Keyword(s):  
Mobile Devices ◽  
Recommender System ◽  
Service Industries ◽  
Classification Rule ◽  
Mobile Service ◽  
Context Aware ◽  
Good Market ◽  
Proposed Model ◽  
Market Opportunities ◽  
Marketing Tool

Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Case Base Elicitation for a Context-Aware Recommender System

2018 ◽  
pp. 170-185
Author(s):  
Jose Luis Jorro-Aragoneses ◽  
Guillermo Jimenez-Díaz ◽  
Juan Antonio Recio-García ◽  
Belén Díaz-Agudo
Keyword(s):  
Recommender System ◽  
Context Aware ◽  
Case Base

scholarly journals Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

2017 ◽  
Vol 7 (12) ◽  
pp. 1211 ◽  
Author(s):  
Khalid Haruna ◽  
Maizatul Akmar Ismail ◽  
Suhendroyono Suhendroyono ◽  
Damiasih Damiasih ◽  
Adi Pierewan ◽  
...  
Keyword(s):  
Recommender System ◽  
Developmental Process ◽  
Research Direction ◽  
Future Research ◽  
Context Aware ◽  
Future Research Direction ◽  
System A

scholarly journals Context-Aware Community Construction in Proximity-Based Mobile Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Na Yu ◽  
Qi Han
Keyword(s):  
Social Networking ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Context Aware ◽  
Two Phase ◽  
Space And Time ◽  
Shared Information ◽  
Influence Graph ◽  
Social Applications ◽  
Share Information

Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom) as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

scholarly journals Context-aware Music Recommender System Based on Implicit Feedback

2021 ◽  
Vol 36 (1) ◽  
pp. WI2-D_1-10
Author(s):  
Yasufumi Takama ◽  
Jing-cheng Zhang ◽  
Hiroki Shibata
Keyword(s):  
Recommender System ◽  
Implicit Feedback ◽  
Context Aware
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