Empirical research on mobile Internet usage: A meta-analysis of the literature

Network congestion during large scale disasters seems inevitable. Sudden influx of traffic caused by users calling their loved ones, coupled with the possibility that many base stations and routers in the area are damaged by the disaster; leads to resource shortage which causes congestion, which in turn causes many users becoming disgruntled and their quality of experience dropping. If a study can be made on predicting user behaviour, based on usual user habits and way of acting immediately after a disaster, then the network provider can regulate traffic accordingly or take precautions in areas within disaster zones.

http://www.icact.org/program/full_paper_counter.asp?full_path=/upload/2014/0076/20140076_finalpaper.pdf

Measure of user behaviour before and during disaster congestion

The mobile Internet is a fast-growing technology that provides access to the traditional stationary fixed-line Internet from devices connected to mobile communication networks. It is predicted that the convergence between mobile networks and the fixed-line Internet will be a core feature in the next generation network architecture, achieving fast 'anywhere' Internet access and global mobility management. Applying a case study approach, this paper reviews the New Zealand mobile Internet market mix, competition, and mobile service provision. The key mobile Internet deployment requirements are determined and analyzed in order to identify a set of mobile Internet critical success factors and to investigate the impact of the shift from fixed-line to mobile and wireless Internet data communication infrastructure.

Deployment and Success Factors for the Mobile Internet: A Case Study Approach

There is much attention given to emerging technologies like mobile internet because of its increasing popularity Much research has concentrated on hardware and some have focused on personalisation in terms of content visualisation The focus of this paper is on mobile content personalisation, seeking to understand the user groups through clustering users based on their profile This paper focuses on the implementation of a technique known as ‘Zoning- Centroid’, which is the evaluation technique used to determine the appropriate number of clusters required to best cluster the given users profile The user profile used in this paper includes mobile content usage and their demographic factors The clustering algorithm used in this paper is k-means clustering The results show that the proposed technique could suggest appropriate number of clusters to be used with the k-values, in order to implement for mobile entertainment content personalisation

/pdf/cluster-analysis-for-personalised-mobile-entertainment-2thy0e2hg1.pdf

Cluster analysis for personalised mobile entertainment content

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate.
 
However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern.
 
An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. 

The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model.
 
Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. 

Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems.

/pdf/an-integrated-mobile-content-recommendation-system-4zq44b3n8t.pdf

An integrated mobile content recommendation system

The mobile Internet emergence gives a new class of opportunities to analyze the user behavior in the environment that is closely related to the users' real lives. The rapid growth in the wireless Internet has a significant dynamic nature, which leads to the difficulty of stable analysis. It is common to witness the drastic traffic change in the mobile Internet. It is important to identify the dynamic transitions of use patterns. User tracking is possible to use the user identifier commonly used in the mobile Internet. From the long-term observation of mobile Internet user transaction logs based on the user identifier, we analyze the long-term usage pattern to identify the metrics to segment various mobile Internet user behaviors. The proposed method uses the number of months in which an end user shows continuous use. The results from the case study and future issues are presented.

T. Yamakami

Papers

Toward understanding the mobile Internet user behavior: a methodology for user clustering with aging analysis