WiBAF : Within Browser Adaptation Framework

The objective of this thesis project is to develop a framework that provides mechanisms to the user to balance personalization and privacy on the web. To achieve this we suggest a browser based adaptation engine that allows the user to choose what portions of her user model will be stored on the client and what will be on the server, trading privacy for personalization. Given that the server can store some data, data-driven adaptation can be enabled, therefore a way to bring together data-driven and expert-driven adaptation has to be defined. Finally it would be interesting to perform meta-adaptation to enhance this data-driven personaliization. Keywords: Adaptation, client-side modeling, data-driven adaptation, expert-driven adaptation, meta-adaptation, server-side modeling, user modeling

Personalized print in Europe

This study focuses on the amount of personalized print produced by printing companies in Europe, the challenges these printers are facing when producing personalized jobs, and how the market for personalized printing in Europe differs from that in the US. Personalization is a marketing tactic in which various media channels are used to send a personalized message to a consumer or client based on their interests. Personalization is not a new tactic. The use of personalization has been slowly increasing over the past few decades to become one of the key tactics used capture a customer\u27s attention. Personalized print--one media channel that can be used in the marketing mix--is divided among different applications such as mail merge, transactional print, versioning, moderate personalized print and highly personalized print. An online survey was distributed to printing companies across Europe via the International Confederation of Printing and Allied Industries (Intergraf). A total of 37 printing companies participated in this study. Survey results revealed that, on average, 6.6% of participants\u27 revenue came from personalized print in 2010. The level of personalization most companies (35%) listed as their major source of revenue was mail merge. The top five challenges that participants were facing related to personalization include: * Communicating the value to our customers of the ROI (Return on Investment) benefits of personalization (45%). * Poor data quality on the client side (30%). * Ability to work with client\u27s marketing decision makers (30%). * Clients do not have retention or customer relationship strategy (30%). * Merging the client\u27s database with variable data software (15%). Though the low response rate from the study restricted the ability to generalize results, an examination of the sample differences revealed that the average amount of revenue obtained from personalized print was over 50% lower in Europe (6.6%) than in the US (20.9%). Note that the data from the US was based solely on digital printers. In Europe, personalization was reported to average around 31.4% of printers\u27 digital revenues, but this data point was from an even smaller sample and the author questions its reliability. The level of personalization that most printing companies indicated, generated a major part of revenue was mail merge, both in Europe (35%) and in the US (61.4%). Lastly, the top challenges, were similar for both the US and Europe. These included: * Communicating the value of personalization. * Poor data quality on the client\u27s side. One explanation for the differences between the US and Europe is buyer behavior--in essence, the marketing culture. Coupons are not used as a marketing tool in most countries in Europe. This could be one of the reasons, that Europe produces less personalized print than the US, where coupons are used on a large scale

Relationship based Entity Recommendation System

With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other entities. These relationships depict more real world relationships between entities, rather than just simple “is-a” or “has-a” relationships. The system keeps track of relationships on which user is clicking and uses this click count as a preference indicator to recommend future entities. This approach is very useful in modern day semantic web searches for recommending entities of user’s interests

Providing Customized Real Time Traffic Information Through the Internet: Implementation Using GIS

For my Masters thesis I implement a web enabled GIS application for presenting personalized real-time traffic condition information. Due to the dynamic nature of traffic condition reporting, often large amounts of data have to be reported. The process of introducing personalization to traffic condition reporting hopes to reduce the amount of such data transmitted to users. The personalization of the presented traffic condition information is achieved by storing geographic definitions of routes and travel zones frequently traveled by the client. Since traffic update areas frequently requested for daily travel routes are often geographically identical, stored routes or zones can be used within a Geographic Information System (GIS) environment to retrieve traffic volume information and visualize the intended route before the start of the client\u27s routine daily trip. This saves both browsing time and data uploading for the client. The research implements such tools in a server-side (most of the processing done on the server) environment. The research concludes that existing GIS tools can be enhanced to implement the concept of using customized traffic profiles to transmit user specific traffic data

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

The simplicity project: easing the burden of using complex and heterogeneous ICT devices and services

As of today, to exploit the variety of different "services", users need to configure each of their devices by using different procedures and need to explicitly select among heterogeneous access technologies and protocols. In addition to that, users are authenticated and charged by different means. The lack of implicit human computer interaction, context-awareness and standardisation places an enormous burden of complexity on the shoulders of the final users. The IST-Simplicity project aims at leveraging such problems by: i) automatically creating and customizing a user communication space; ii) adapting services to user terminal characteristics and to users preferences; iii) orchestrating network capabilities. The aim of this paper is to present the technical framework of the IST-Simplicity project. This paper is a thorough analysis and qualitative evaluation of the different technologies, standards and works presented in the literature related to the Simplicity system to be developed

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

A hybrid strategy for privacy-preserving recommendations for mobile shopping

To calculate recommendations, recommender systems col-lect and store huge amounts of users ’ personal data such as preferences, interaction behavior, or demographic infor-mation. If these data are used for other purposes or get into the wrong hands, the privacy of the users can be com-promised. Thus, service providers are confronted with the challenge of o↵ering accurate recommendations without the risk of dissemination of sensitive information. This paper presents a hybrid strategy combining collaborative filtering and content-based techniques for mobile shopping with the primary aim of preserving the customer’s privacy. Detailed information about the customer, such as the shopping his-tory, is securely stored on the customer’s smartphone and locally processed by a content-based recommender. Data of individual shopping sessions, which are sent to the store backend for product association and comparison with simi-lar customers, are unlinkable and anonymous. No uniquely identifying information of the customer is revealed, making it impossible to associate successive shopping sessions at the store backend. Optionally, the customer can disclose demo-graphic data and a rudimentary explicit profile for further personalization