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This paper presents an explorative investigation into households' uses of traditional broadcast television (TV) and more recently introduced video-on-demand (VoD) services. More specifically, we explain how each way of viewing TV and video content relates to different viewing situations in the home. We conducted in-home interviews with seven households in The Netherlands in order to obtain rich data that are required for understanding these phenomena. Our results elaborate on the uses of watching broadcast TV, catch-up services, and video-on-demand streaming services, the recording of content, and the downloading of content. While the traditional broadcast model is on the decline to some extent, our data still revealed essential uses of broadcast concerning certain types of content and specific viewing situations. Based on the results, a number of implications for the design of recommender systems and interfaces, service providers and broadcasters, and TV manufacturers are presented.

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In this paper, we present a better understanding of the contextual aspects that determine TV and video viewing situations in the home. The results can be used to design recommender systems algorithms and interfaces for TV and video content that better fits with different viewing situations in the home. This is achieved by taking into account these typical viewing situations and the respective manifestations of contextual factors. In a first, ethnographic, study with 12 households to better understand everyday viewing practices, we obtained insights into the relation between the type of content and the amount of attention paid, the type of content and planned versus spontaneous behaviour, the role of the structure of the household, and the way people discover content. In a second, multi-method, study with seven households, we identified seven typical viewing situations and elaborated on how four important contextual factors-time, mood, content and viewers-constitute these viewing situations or experiences in the home. After combining the results from both studies, two additional contextual aspects were added: content delivery type and viewing mode. The insights from these studies allow us to suggest opportunities for the design of recommender system algorithms that take into account the four contextual aspects and to formulate implications for the design of recommender interfaces.

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalised search and content retrieval.On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the user's personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience.In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the user's behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories.In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations Preprint submitted to Journal of Network and Computer Applications October 26, 2009 allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the user's interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.

Abstract-The scope of this paper is the interdisciplinary measurement and modeling methodology of Quality of Experience (QoE) when playing a mobile location-based massively multiplayer online role-playing game (MMORPG) that places the virtual world on top of the real world using the user's location. The paper introduces the implementation of a re-usable mobile QoE measurement framework on the Android platform and illustrates how it was applied in a concrete case-study. In this respect, a multidimensional QoE prediction model consisting of both objective and subjective parameters is presented. In this model, test users' evaluations regarding feelings of amusement, absorption or engagement experienced while playing a popular location based mobile game (Parallel Kingdom) are taken into account and related to a set of objective QoS-related parameters, contextual data, and physiological data obtained from an on-body sensor. The latter evidences the intensity of physical activity of the test users during the gaming session, taking into account Metabolic Equivalent of Task (MET) measurements. Results of this study indicate that the test users' QoE was influenced by their physical effort, the data connection type used, and the playing context (e.g. interaction with other players). Despite of the fact of using a re-usable framework for QoE estimation, the analysis is limited to this particular popular (more than 100000 users) mobile game. QoE; QoS; Google Android platform; on-body sensor; 3G; location-based (LB) mobile game; massively multiplayer online role-playing game (MMORPG)

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