CORRELATION OF CONTEXT INFORMATION FOR MOBILE SERVICES

The advancements in mobile technologies make the collection of customers’ context information feasible. Service providers can now incorporate context information of customers when providing personalized services to them. This type of services is called context sensitive mobile services (CSMS). Context refers to the environment around customers when there are business transactions between customers and service providers. Location, time, mobile device, services, and other application specific information are all possible components of context. Compared to other types of mobile services, CSMS can fit to customers’ demands better. CSMS can follow push model or pull model. Different context sensitive services are sensitive to different context information with different degrees of sensitivity. In the future, CSMS can find good support from data mining approaches to understand customers better. Security is currently an important issue for CSMS.

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Modeling Context Information for Realizing Simple Mobile Services

2007 16th IST Mobile and Wireless Communications Summit ◽

10.1109/istmwc.2007.4299149 ◽

2007 ◽

Cited By ~ 5

Author(s):

Gregor Broll ◽

Heinrich HuBmann ◽

George N. Prezerakos ◽

Georgia Kapitsaki ◽

Stefano Salsano

Keyword(s):

Mobile Services ◽

Context Information

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Automatic Optimizer Generation Method Based on Location and Context Information to Improve Mobile Services

Mobile Information Systems ◽

10.1155/2017/2835163 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7

Author(s):

Yunsik Son ◽

Junho Jeong ◽

Yangsun Lee

Keyword(s):

Mobile Devices ◽

Mobile Services ◽

Location Based Services ◽

Mobile Systems ◽

Context Information ◽

Significant Issue ◽

Application Service ◽

Battery Power ◽

Service Type ◽

Battery Consumption

Several location-based services (LBSs) have been recently developed for smartphones. Among these are proactive LBSs, which provide services to smartphone users by periodically collecting background logs. However, because they consume considerable battery power, they are not widely used for various LBS-based services. Battery consumption, in particular, is a significant issue on account of the characteristics of mobile systems. This problem involves a greater service restriction when performing complex operations. Therefore, to successfully enable various services based on location, this problem must be solved. In this paper, we introduce a technique to automatically generate a customized service optimizer for each application, service type, and platform using location and situation information. By using the proposed technique, energy and computing resources can be more efficiently employed for each service. Thus, users should receive more effective LBSs on mobile devices, such as smartphones.

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Music Recommendation with Collaborative Filtering for Mobile Services

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.519-520.510 ◽

2014 ◽

Vol 519-520 ◽

pp. 510-515 ◽

Cited By ~ 1

Author(s):

Ya Fei Tang ◽

Yun Yong Zhang ◽

Jin Wu Wei ◽

Xiao Ming Chen

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Matrix Completion ◽

Mobile Services ◽

Context Information ◽

Music Recommendation ◽

Computational Capability ◽

The Matrix ◽

Online Music ◽

Offline Processing

As the development of the mobile communication and the computational capability of the mobile terminals, more users use their mobile devices to play music. In this work, an online music recommendation system is designed for mobile services, which consists of two modules: offline processing and online recommendation. The offline module labels all the music into different categories, by which the music items libraries corresponding to the tags are constructed and the rating matrixs are consequently built. The online module integrates the context information, by which the matched rating matrix is retrieved. By using the collaborative filtering model with matrix completion algorithm, the music recommendations that suit the user and the situation are offered. The proposed recommendation system improves the precision of the recommendation by integration the context information of the users, and augments the online computational capability because the matrix scale is reduced by constructing the rating matrices for the music in the different tag libraries. A large number of experiments demonstrate that the proposed system is designed to be robust and effective to the music recommendation and efficient to the online recommendation for the mobile services.

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Automatic Stereotype Activation Is Context Dependent

Social Psychology ◽

10.1027/1864-9335/a000019 ◽

2010 ◽

Vol 41 (3) ◽

pp. 131-136 ◽

Cited By ~ 35

Author(s):

Catharina Casper ◽

Klaus Rothermund ◽

Dirk Wentura

Keyword(s):

Lexical Decision ◽

Context Information ◽

Decision Task ◽

Social Categories ◽

Priming Effects ◽

Stereotype Activation ◽

Social Stereotypes ◽

Priming Paradigm ◽

Automatic Activation ◽

Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

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Impact of Context Information on Metaphor Elaboration

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000422 ◽

2018 ◽

Vol 65 (6) ◽

pp. 370-384 ◽

Cited By ~ 5

Author(s):

Veronika Lerche ◽

Ursula Christmann ◽

Andreas Voss

Keyword(s):

Diffusion Model ◽

Processing Speed ◽

A Priori ◽

Model Fit ◽

Verbal Task ◽

Context Information ◽

Speed Of Processing ◽

Good Account ◽

Dependent Variables ◽

Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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