Segmentazione e modellazione del comportamento degli utenti di un portale per la ricerca del lavoro

L'obiettivo di questa tesi è quello di studiare, da un punto di vista metodologico e analitico, il comportamento degli utenti che visitano il motore di ricerca di Jobrapido, con lo scopo di profilarli e segmentarli per migliorare la loro esperienza sul sito e fornire informazioni di supporto al management

An integrated mobile content recommendation system

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

Modelling Web Usage in a Changing Environment

Eiben, A.E. [Promotor]Kowalczyk, W. [Copromotor

Customer profiling using a service-orientated architecture

Customer profiling has recently gained much recognition in the e-commerce domain because of the benefits it is capable of bringing to online business. Customer profiling has been implemented in various systems development approaches such as in a client-server environment. Recently there has been an increase in the number of organisations adopting and implementing e-commerce systems using service-oriented architecture (SOA) principles. This research set out to determine how a customer profile can be implemented using open source SOA implementation tools, and how SOA-based customer profiles can be utilised to provide appropriate personalisation in an SOA environment. The research further endeavoured to complete a comparative study on customer profile implementation in two different architectures, namely SOA and client-server. An extensive literature review was conducted on SOA, customer profiling and e-commerce systems development. SOA enabling technologies, such as, web services, enterprise service bus (ESB) and open source Sun Java SOA implementation tools, for example, Open ESB, GlassFish application server and Netbeans IDE were analysed. A Java web services-based customer profiling system was prototyped following SOA design principles. An end-user evaluation survey was conducted using eye tracking with a sample of 30 participants. The evaluation was done on two e-commerce systems with the same interface but running on two different customer profile back-ends, SOA and client-server. The results show that participants did not experience significant difference between the two systems, however, eye tracking results showed a significant difference between the two systems. The research concluded that customer profiling using SOA offers more benefits than implementations using other architectures such as client-server. SOA component-based development proved to be easier to manage, develop, integrate and improves interoperability between different technologies. The research brought together necessary techniques and technologies that organisations can use to implement SOA. Using SOA, organisations can integrate and utilise different technologies seamlessly to achieve business goals