Tom Deryckere

Proposed framework for evaluating quality of experience in a mobile, testbed-oriented living lab setting

The framework presented in this paper enables the evaluation of Quality of Experience (QoE) in a mobile, testbed-oriented Living Lab setting. As a result, it fits within the shift towards more user-centric approaches in innovation research and aims to bridge the gap between technical parameters and human experience factors. In view of this, Quality of Experience is seen as a multi-dimensional concept, which should be considered from an interdisciplinary perspective. Although several approaches for evaluating perceived QoE have been proposed in the past, they tend to focus on a limited number of objective dimensions and fail to grasp the subjective counterparts of users’ experiences. We therefore propose a distributed architecture for monitoring network Quality of Service (QoS), context information and subjective user experience based on the functional requirements related to real-time experience measurements in real-life settings. This approach allows us to evaluate all relevant QoE-dimensions in a mobile context.

Context aware recommendations for user-generated content on a social network site

The enormous offer of video content on the internet and its continuous growth make the selection process increasingly difficult for end-users. This overabundance of audio-visual material can be handled by a recommendation system that observes user preferences and assists people with finding interesting content. However, present-day recommendation systems focus on the metadata or the previous consumption behaviour to select the content but do not consider contextual information or social network relations. Therefore, we developed a tag cloud based recommendation system for user-generated content which exploits these social network relations. Recommendations based on the users profile are supplemented with social recommendations: content suggestions from people on the users contact list. Moreover, since we believe that the consumption context (location, time, etc.) has a significant influence on the content selection process, the system records all the available context information. Our next task is to analyze the obtained dataset and to determine the influence of the individual context features on the consumption behaviour. The system recommendations and social recommendations will be compared on the basis of effectiveness, novelty and user appreciation. Finally, we intend to incorporate the results of this analysis in our personalization algorithm in order to improve the recommendation results.

User-driven innovation? Challenges of user involvement in future technology analysis

The shift from the traditional push towards more user-driven innovation strategies in the information and communications technologies domain has urged companies to place the user at the core of their innovation process in a more systematic way. In this paper we reflect on the implications of this new innovation context for traditional product development processes. Given these implications, two challenges are discussed that are crucial to true user-driven innovation, i.e. the challenge of continuously involving the user and the need for tools to facilitate the integration of knowledge into the increasingly interdisciplinary development process. Drawing on our own experiences in the interdisciplinary Research On Mobile Applications and Services (ROMAS) project, which focused on future mobile applications in a living lab setting, we illustrate how the two challenges can be tackled. Copyright , Beech Tree Publishing.

Proposed architecture and algorithm for personalized advertising on iDTV and mobile devices

The advances in digital media entail an increase in the number of interactive advertising channels: Internet, interactive digital television (iDTV) and mobile applications are gaining importance in the advertisement sector. New interactive advertisement formats can complement the traditional commercial breaks and increase the revenues. Personalization is a promising technique to reach the target audience precisely, get a greater response and increase the return on investment. In this article we present an architecture that offers personalized commercials for iDTV, internet, and mobile devices. By logging the user activity on the three platforms, the system constructs a detailed profile usable for commercial targeting. This personal profile, supplemented with the community behavior and the metadata about the commercials, makes up the data source for the personalization algorithm.

Linking users' subjective QoE evaluation to signal strength in an IEEE 802.11b/g wireless LAN environment

Although the literature on Quality of Experience (QoE) has boomed over the last few years, only a limited number of studies have focused on the relation between objective technical parameters and subjective user-centric indicators of QoE. Building on an overview of the related literature, this paper introduces the use of a software monitoring tool as part of an interdisciplinary approach to QoE measurement. In the presented study, a panel of test users evaluated a mobile web-browsing application (i.e., Wapedia) on a PDA in an IEEE 802.11b/g Wireless LAN environment by rating a number of key QoE dimensions on the device immediately after usage. This subjective evaluation was linked to the signal strength, monitored during PDA usage at four different locations in the test environment. The aim of this study is to assess and model the relation between the subjective evaluation of QoE and the (objective) signal strength in order to achieve future QoE optimization.

Extending the Bayesian Classifier to a Context-Aware Recommender System for Mobile Devices

Mobile devices that are capable of playing Internet videos have become wide-spread in recent years. Because of the enormous offer of video content, the lack of sufficient presentation space on the screen, and the laborious navigation on mobile devices, the video consumption process becomes more complicated for the end-user. To handle this problem, people need new instruments to assist with the hunting, filtering and selection process. We developed a methodology for mobile devices that makes the huge content sources more manageable by creating a user profile and personalizing the offer. This paper reports the structure of the user profile, the user interaction mechanism, and the recommendation algorithm, an improved version of the Bayesian classifier that incorporates aspects of the consumption context (like time, location, and mood of the user) to make the suggestions more accurate.

A software tool to relate technical performance to user experience in a mobile context

Users in todaypsilas mobile ICT environment are confronted with more and more innovations and an ever increasing technical quality, which makes them more demanding and harder to please. It is often hard to measure and to predict the user experience during service consumption. This is nevertheless a very important dimension that should be taken into account while developing applications or frameworks. In this paper we demonstrate a software tool that is integrated in a wireless living lab environment in order to validate and quantify actual user experience. The methodology to assess the user experience combines both technological and social assets. User experience of a Wineguide application on a PDA is related to signal strength, monitored during usage of the applications. Higher signal strengths correspond with a better experience (e.g. speed). Finally, difference in the experience among users will be discussed.

Time dependency of data quality for collaborative filtering algorithms

The efficiency of personal suggestions generated by collaborative filtering techniques is highly dependent on the quality and quantity of the available consumption data. Extending data sets with additional consumption data (from the past) might enrich the user profiles and generally leads to more accurate recommendations. Although if a considerable amount of profile information is already available and detailed personal preferences can be derived, supplementary consumption data may not have any (or a very limited) added value for the recommendation algorithm. These additional consumption data increase the required storage capacity and the computational load to generate the personal recommendations. Moreover, since personal preferences and the relevance of content items may vary over time, older consumption data might be outdated and lead to inaccurate recommendations. Therefore, we investigate which consumption data are (the most) relevant to feed the conventional collaborative filtering algorithms. For provider-generated content systems, we demonstrate that the accuracy of collaborative filtering algorithms increases by extending user profiles with additional older consumption data. In contrast, we witness the opposite effect for user-generated content systems: involving older consumption data has a negative influence on the recommender accuracy. These results are important for website owners who intend to employ a recommendation system at a minimum storage and computation cost.

XMPP and iDTV or How to Make Television a Social Medium

Instant Messaging (IM) has the potential to become one of the killer applications for interactive Digital Television (iDTV) [12]. However, several factors make it difficult to provide a good implementation of IM services, among which the limited resources of a settop box and the different user experience compared to computer environments. This paper proposes the XMPP (Extensible Messaging and Presence Protocol) standard as a solution for implementing IM. When we compare XMPP with other technologies, it reveals itself to be very well adapted to the specific needs of iDTV middleware platforms like the Multimedia Home Platform (MHP) [5]. Moreover, the use of XMPP doesn’t limit itself to IM. The flexible architecture of XMPP opens a window of opportunities like the ease of adding new interactive services. To demonstrate the possibilities of XMPP on MHP, an IM client –IM4MHP– is presented in this paper.

Extending User Profiles in Collaborative Filtering Algorithms to Alleviate the Sparsity Problem

The overabundance of information and the related difficulty to discover interesting content has complicated the selection process for endusers. Recommender systems try to assist in this content-selection process by using intelligent personalisation techniques which filter the information. Most commonly-used recommendation algorithms are based on Collaborative Filtering (CF). However, present-day CF techniques are optimized for suggesting provider-generated content and partially lose their effectiveness when recommending user-generated content. Therefore, we propose an advanced CF algorithm which considers the specific characteristics of user-generated content (like the sparsity of the data matrix). To alleviate this sparsity problem, profiles are extended with probable future consumptions. These extended profiles increase the profile overlap probability, thereby increasing the number of neighbours used for calculating the recommendations. This way, the recommendations become more precise and diverse compared to traditional CF recommendations. This paper explains the proposed algorithm in detail and demonstrates the improvements on standard CF.