Modeling and Analyzing User Contexts for Mobile Advertising

2011 ◽  
Vol 2 (3) ◽  
pp. 38-52
Author(s):  
Nan Jing ◽  
Yong Yao ◽  
Yanbo Ru
Keyword(s):  
Mobile Devices ◽  
Online Advertising ◽  
Research Work ◽  
User Interaction ◽  
Location Tracking ◽  
Context Aware ◽  
Mobile Advertising ◽  
Mobile Platforms ◽  
User Context ◽  
Critical Components

Context-aware advertising is one of the most critical components in the Internet ecosystem today because most WWW publisher revenue highly depends on the relevance of the displayed advertisement to the context of the user interaction. Existing research work focuses on analyzing either the content of the web page or the keywords of the user search. However, there are limitations of these works when being extended into mobile computing domain, where mobile devices can provide versatile contexts, such as locations, weather, device capability, and user activities. These contexts should be well categorized and utilized for online advertising to gain better user experience and reaction. This paper examines the aforementioned limitations of the existing works in context-aware advertising when being applied for mobile platforms. A mobile advertising system is proposed, using location tracking and context awareness to provide targeted and meaningful advertisement to the customers on mobile devices. The three main modules of this comprehensive mobile advertising system are discussed, including advisement selection, advertisement presentation, and user context databases. A software prototype that is developed to conduct the case studies and validate this approach is presented.

Modeling and Analyzing User Contexts for Mobile Advertising

2013 ◽  
pp. 93-107
Author(s):  
Nan Jing ◽  
Yong Yao ◽  
Yanbo Ru
Keyword(s):  
Mobile Devices ◽  
Online Advertising ◽  
Research Work ◽  
User Interaction ◽  
Context Aware ◽  
Web Page ◽  
Mobile Advertising ◽  
Mobile Platforms ◽  
User Context ◽  
Critical Components

Context-aware advertising is one of the most critical components in the Internet ecosystem today because most WWW publisher revenue highly depends on the relevance of the displayed advertisement to the context of the user interaction. Existing research work focuses on analyzing either the content of the web page or the keywords of the user search. However, there are limitations of these works when being extended into mobile computing domain, where mobile devices can provide versatile contexts, such as locations, weather, device capability, and user activities. These contexts should be well categorized and utilized for online advertising to gain better user experience and reaction. This paper examines the aforementioned limitations of the existing works in context-aware advertising when being applied for mobile platforms. A mobile advertising system is proposed, using location tracking and context awareness to provide targeted and meaningful advertisement to the customers on mobile devices. The three main modules of this comprehensive mobile advertising system are discussed, including advisement selection, advertisement presentation, and user context databases. A software prototype that is developed to conduct the case studies and validate this approach is presented.

A User Context-Aware Advertising Framework for the Mobile Web

2010 ◽  
pp. 1-15
Author(s):  
Nan Jing ◽  
Yong Yao ◽  
Yanbo Ru
Keyword(s):  
Mobile Devices ◽  
Online Advertising ◽  
User Interaction ◽  
Context Aware ◽  
Mobile Platforms ◽  
Social Community ◽  
Mobile Web ◽  
Web Technologies ◽  
Critical Components ◽  
Advertising Content

Context-aware advertising is one of the most critical components in the Internet ecosystem today because most WWW publisher’s revenue highly depends on the relevance of the displayed advertisement to the context of the user interaction. Existing research works in context-aware advertising mainly focus on analyzing either the content of the web page (in which it is also called contextual advertising), or the keywords of the user search. However, we have identified the limitations of these works when being extended into mobile web, which has become a major platform for users to access Internet with thanks to the new lightweight web technologies and the development of mobile devices. These mobile devices are equipped with networking capabilities and sensors that provide versatile contexts including physical environment, user internal and social community. These contexts, which are far beyond just page content and search keywords, should be well organized and utilized for online advertising to gain better user experience and reaction. In this chapter, we point out the aforementioned limitations of the existing works in context-aware advertising when being applied for mobile platforms. We also discuss the characteristics of the contexts that are available on mobile devices and clearly describe the challenges of utilizing these contexts to optimize the advertisement on mobile platforms. We then present a context-aware advertising framework that collects and integrates the user contexts to select, generate, and present advertising content. The purpose of this framework is to provide the mobile users with targeted and purposeful advertisement. Finally, we discuss the implementation aspects and one specific application of this framework and outline our future plans.

Context-Aware Personalization for Mobile Services

2019 ◽  
pp. 818-830
Author(s):  
Abayomi Moradeyo Otebolaku ◽  
Maria Teresa Andrade
Keyword(s):  
Mobile Devices ◽  
Information Overload ◽  
Special Kind ◽  
Context Aware ◽  
Mobile Platforms ◽  
Content Consumption ◽  
Mobile Information ◽  
Personal Content ◽  
Mobile Context ◽  
The Web

Audiovisual content consumption on mobile platforms is rising exponentially, and this trend will continue in the next years as mobile devices become more sophisticated. Thus, smartphones are gradually replacing our desktops as they increasingly become cheaper and more powerful with excellent multimedia processing support. As mobile users go about their routines, they continuously browse the web, seeking interesting content to consume, and also uploading personal content. However, users encounter huge volume of content that does not match their preferences, resulting in mobile information overload. Context-aware media personalization (CAMP) was proposed as a solution to this problem. CAMP assists users to select relevant content among alternatives considering users' preferences and contexts. This solution, however, are limited to static contexts. The contribution is mobile context-aware media personalization (MobCAMP), which is a special kind of personalization that utilizes users' contexts and activities to suggest media content according to the user's tastes and contextual situations.

scholarly journals Rule-based solution for context-aware reasoning on mobile devices

2014 ◽  
Vol 11 (1) ◽  
pp. 171-193 ◽  
Author(s):  
Grzegorz Nalepa ◽  
Szymon Bobek
Keyword(s):  
Mobile Devices ◽  
Rapid Evolution ◽  
Preliminary Evaluation ◽  
Context Aware ◽  
Context Modeling ◽  
Network Access ◽  
Mobile Platforms ◽  
Rule Based ◽  
Real Time Processing ◽  
Context Reasoning

With the rapid evolution of mobile devices, the concept of context aware applications has gained a remarkable popularity in recent years. Smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, and GPS, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows real-time processing of data gathered by their sensors. Universal network access viaWiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Most of existing frameworks for context-aware systems, are usually dedicated to static, centralized, clientserver architectures. However, mobile platforms require from the context modeling language and inference engine to be simple and lightweight. The model should also be powerful enough to allow not only solving simple context identification tasks but more complex reasoning. The original contribution of the paper is a proposal of a new rule-based context reasoning platform tailored to the needs of such intelligent distributed mobile computing devices. It contains a proposal of a learning middleware supporting context acquisition. The platform design is based on a critical review and evaluation of existing solutions given in this paper. A preliminary evaluation of the platform is given along with use cases including a social system supporting crime detection and investigation.

scholarly journals Online Video Platform with Context-aware Content-based Recommender System

2021 ◽  
Vol 6 (1) ◽  
pp. 46-53
Author(s):  
Marian Pisotskyi ◽  
◽  
Alexey Botchkaryov
Keyword(s):  
User Interface ◽  
Recommender System ◽  
User Interaction ◽  
User Preferences ◽  
Online Video ◽  
Context Aware ◽  
User Context

The problem of developing an online video platform with a context-aware content-based recommender system has been considered. Approaches to developing online video platforms have been considered. A comparison of popular online video platforms has been presented. A method of context-aware content-based recommendation of videos has been proposed. A method involves saving information about user interaction with video, obtaining and storing information about which videos the user liked, determining user context, composing a profile of user preferences, composing a profile of user preferences depending on context, determining the similarity between the video profile and a profile of user preferences (with and without context consideration), determining the relevance of the video to the context, the conclusive estimation of the relevance of the video to the user’s preferences based on the proposed composite relevance indicator. The developed structure of online video platform has been presented. The algorithm of its work has been considered. The structure of the online video platform database has been proposed. Features of designing the user interface of the online video platform have been considered. The issue of testing the developed online video platform has been considered.

Context-Aware Personalization for Mobile Services

2018 ◽  
pp. 6031-6042
Author(s):  
Abayomi Moradeyo Otebolaku ◽  
Maria Teresa Andrade
Keyword(s):  
Mobile Devices ◽  
Information Overload ◽  
Special Kind ◽  
Context Aware ◽  
Mobile Platforms ◽  
Content Consumption ◽  
Mobile Information ◽  
Personal Content ◽  
Mobile Context ◽  
The Web

Audiovisual content consumption on mobile platforms is rising exponentially and this trend will continue in the next years as mobile devices become more sophisticated. Thus, smartphones are gradually replacing our desktops as they increasingly become cheaper and more powerful with excellent multimedia processing support. As mobile users go about their routines, they continuously browse the Web, seeking interesting content to consume, and also uploading personal content. However, users encounter huge volume of content, that does not match their preferences, resulting in mobile information overload. Context-aware media personalization (CAMP) was proposed as a solution to this problem. CAMP assists users to select relevant content among alternatives considering users' preferences and contexts. This solution, however, are limited to static contexts. Our contribution is Mobile Context-Aware Media Personalization(MobCAMP), which is a special kind of personalization that utilizes user's contexts and activities to suggest media content according to the user's tastes and contextual situations.

Context-Aware Mobile Learning on the Semantic Web

2011 ◽  
pp. 23-44 ◽  
Author(s):  
Rachid Benlamri ◽  
Jawad Berri ◽  
Yacine Atif
Keyword(s):  
Semantic Web ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Research Work ◽  
Learning Resources ◽  
Context Aware ◽  
Semantic Level ◽  
Contextual Elements ◽  
Smart Mobile ◽  
Interaction History

This chapter focuses on the theoretical and technological aspects of designing mobile learning (m-learning) services that deliver context-aware learning resources from various locations and devices. Context-aware learning is an important requirement for next generation intelligent m-learning systems. The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this research work, context reflects timeliness and mobility to nurture pervasive instruction throughout the learning ecosystem. In this context of ubiquity that is supported by a new generation of mobile wireless networks and smart mobile devices, it is clear that the notion of context plays a fundamental role since it influences the computational capabilities of the used technology. In particular, three types of context awareness are being considered in this work —platform-awareness, learner-awareness, and task-awareness. In this research work, these contextual elements are defined at the semantic level in order to facilitate discoverability of context-compliant learning resources, adaptability of content and services to devices of various capabilities, and adaptability of services to task at hand and interaction history. The work presented in this chapter contributes towards this direction, making use of the progress in Semantic Web theory and mobile computing to enable context-aware learning that satisfies learning timeliness and mobility requirements.

User Interaction with Context-aware Recommender Systems on Smartphones

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Wolfgang Wörndl ◽  
Béatrice Lamche
Keyword(s):  
Recommender Systems ◽  
Mobile Devices ◽  
User Experience ◽  
Evaluation Criteria ◽  
User Interaction ◽  
Objective Evaluation ◽  
Preference Elicitation ◽  
Context Aware ◽  
Mobile Context ◽  
Selection Of

SummaryIn this article we give an overview on selected aspects of user interaction with context-aware recommender systems on smartphones. We discuss these according to the three steps of user interaction with recommender systems using subjective and objective evaluation criteria: 1. Preference elicitation: how input methods on mobile devices can influence the users’ rating behavior, 2. Result delivery and presentation: how results can be adapted to the mobile context, 3. Feedback, critiquing and refinement: how interactive explanation can improve the user experience. The selection of examples is based on several studies we did in different mobile scenarios.

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.

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