Mobile content personalisation using intelligent user profile approach

As there are several limitations using mobile internet, mobile content personalisation seems to be an alternative to enhance the experience of using mobile internet. In this paper, we propose the mobile content personalisation framework to facilitate collaboration between the client and the server. This paper investigates clustering and classification techniques using K-means and Artificial Neural Networks (ANN) to predict user's desired content and WAP pages based on device's listed-oriented menu approach. We make use of the user profile and user's information ranking matrix to make prediction of the desired information for the user. Experimental results show that it can generate promising prediction. The results show that it works best when used for predicting 1 matched menu item on the screen

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

Mobile device can be used as a medium to send and receive the mobile internet content. However, there are several limitations using mobile internet. Content personalisation has been viewed as an important area when using mobile internet. In order for personalisation to be successful, understanding the user is important. In this paper, we explore the implementation of the user profile at client-side, which may be used whenever user connect to the mobile content provider. The client-side user profile can help to free the provider in performing analysis by using data mining technique at the mobile device. This research investigates the conceptual idea of using clustering and classification of user profile at the client-site mobile. In this paper, we applied K-means and compared several other classification algorithms like TwoStep, Kohenen and Anomaly to determine the boundaries of the important factors using information ranking separation

A model for mobile content filtering on non-interactive recommendation systems

To overcome the problem of information overloading in mobile communication, a recommendation system can be used to help mobile device users. However, there are problems relating to sparsity of information from a first-time user in regard to initial rating of the content and the retrieval of relevant items. In order for the user to experience personalized content delivery via the mobile recommendation system, content filtering is necessary. This paper proposes an integrated method by using classification and association rule techniques for extracting knowledge from mobile content in a user's profile. The knowledge can be used to establish a model for new users and first rater on mobile content. The model recommends relevant content in the early stage during the connection based on the user's profile. The proposed method also facilitates association to be generated to link the first rater items to the top items identified from the outcomes of the classification and clustering processes. This can address the problem of sparsity in initial rating and new user's connection for non-interactive recommendation systems

A framework for integrated mobile content recommendation

Content filtering in a mobile recommendation system plays a vital role in providing solution to help mobile device users obtain their desire content. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. These problems can be viewed as a user’s first time connection to a mobile recommendation system and initial rating of the content in an early stage of the system. Hence, to obtain personalized content for mobile user, mobile content filtering is needed. This paper proposes a framework for integrated mobile content recommendation. The framework makes use of classification and adaptive association rule techniques to build an integrated model. The results demonstrate that the proposed framework outperforms related techniques. This can address the problem of sparsity for mobile content recommendation systems

Time-based mobile content usage personalisation

Many limitations of the mobile devices and the content presentation screen size when connecting the mobile internet tend to be difficult for mobile users to handle the amount of information flowing to them. They have to scroll down several levels in order to obtain the most needed content. This paper proposes a personalised content menu system that can bring the desire content for user by using the period-of-day information to facilitate the mobile internet usage. Users should not scroll down several levels from the list-oriented menu to obtain their interested information. Moreover, by using the period-of-day information, the more desirable content can be display by using the users’ lifestyle profile in order to deliver the content that are more relevant to the users at that time. The result shows the proposed mobile menu system which could provide around 80% accuracy in achieving the personalization experience and this paper also presented the concept to create the mobile personalization

Adaptive mobile content personalisation using the time-of-day

The limitations of the hardware of the mobile devices and the content presentation screen size when using the mobile internet seem to be difficult for mobile users to handle the amount of information flowing to them. They have to scroll down several levels in order to obtain the most desired content. This paper proposes a personalised adaptive menu system that can bring the desire content for user by using the time-of-day information to facilitate the mobile internet usage. Users should not scroll down several levels from the list-oriented menu to obtain their interested information. Furthermore, by using the time-of-day information, the more desirable content can be display by using the users‟ lifestyle profile in order to deliver the content that are more relevant to the users at that time.. The results also show that the proposed adaptive menu system could provide around 80% accuracy in achieving the personalization experience

Intelligent mobile user profile classification for content personalisation

Mobile device can be used as a medium to send and receive mobile internet content. Although there are several limitations using mobile internet, content personalisation have been viewed as an important area for advancing mobile internet. In this paper we implement the concept of using clustering and classification for user profile based on user's information ranking with demographic factors. K-means is applied as a clustering technique while Artificial Neural Networks (ANN) is used to predict user's desired content. Experimental tests have been carried out to demonstrate the proposed method and results show that it can generate promising results

Cluster analysis for personalised mobile entertainment content

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

Time-based personalised mobile game downloading

Mobile device is an important gadget for many people today, regardless whether it is used for communication, entertainment or keeping up to date. With the advancement of mobile Internet, mobile devices are also used for many conventional PC based Internet applications. One such example is downloading games. Downloading mobile games via mobile device seems to be one of the more favorite activities for many mobile devices today. However, users may have different preferences in what genres of games they will be more interested in at different time of the day. In this paper, a personalised mobile game recommendation system which takes into consideration the time-of-day and time-of-week is used to provide a more personalised experience. From the data collected, it can be seen that at different time periods users may download different games and from different game genres