Client-side mobile user profile for content management using data mining techniques

Paireekreng, Worapat; Wong, Kok Wai

doi:10.1109/snlp.2009.5340939

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…17 Here authors focused on the content personalization to help mobile users retrieve information and services efficiently. 19 In their proposed two-phase framework, clustering was used to construct a user profile, while classification was used to classify user profiles based on the class information from clustering. In this work, K-means, TwoStep, Anomaly and Kohenen clustering algorithms were compared for clustering.…”

Section: Related Workmentioning

confidence: 99%

Feature weighted clustering for user profiling

Cufoglu

Lohi

Everiss

2017

Int. J. Model. Simul. Sci. Comput.

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Personalization is the adaptation of the services to fit the user's interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user's personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.

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Section: Related Workmentioning

confidence: 99%

Feature weighted clustering for user profiling

Cufoglu

Lohi

Everiss

2017

Int. J. Model. Simul. Sci. Comput.

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“…about me, interests). In another work [43], Paireekreng and Wong investigated the use of clustering and classification of user profile at the client-side mobile. Here, the authors focused on the content personalization to help mobile users retrieve information and services efficiently.…”

Section: Classification and Clustering Algorithms For User Profilingmentioning

confidence: 99%

User Profiling - A Short Review

Cufoglu¹

2014

IJCA

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In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. Furthermore, it also give details about the simulations which have been carried out with well known classification and clustering algorithms with real world user profile dataset. This work is based on the doctoral thesis of the author.

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“…This is also a simple algorithm with much difference adaptation for applications [2]. The clustering component in [16,17] also show the mobile user clustering using demographic factors and information ranking to filter the cluster.…”

Section: Mobile Content Personalisationmentioning

confidence: 99%

Cluster Analysis for Personalised Mobile Entertainment Content

Paireekreng

Wong

Fung

2010

IFIP Advances in Information and Communication Technology

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Abstract. 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 on profile. This paper focuses on the implementation of a technique known as 'ZoningCentroid', which is the evaluation technique used to determine the appropriate number of clusters that best cluster the given users profile. The user profile used in this paper includes mobile content usage based on basic demographic factors. The algorithm used to cluster in this paper is k-means clustering. The results show that the proposed technique could suggest the appropriate number of clusters to be used with the k-values for implementing in the area of mobile entertainment content personalisation.

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Client-side mobile user profile for content management using data mining techniques

Cited by 9 publications

References 11 publications

Feature weighted clustering for user profiling

Feature weighted clustering for user profiling

User Profiling - A Short Review

Cluster Analysis for Personalised Mobile Entertainment Content

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