Analysis of IPTV Channels Watching Preferences in Bosnia and Herzegovina

IPTV service is a new service which is today offered from almost every Telecom operator. One of the advantages of IPTV service stemming from its architecture is certainly the fact that it is very easy to measure what TV channels are the ones mostly watched. This papers desribes this measurement and analysis results in one Telecom operator in Bosnia and Herzegovina. They describe what TV channels are mostly watched in different time periods. We developed a simple weighting algorithm to order the channels by watching rate. Based on it we are providing extensive tables. This paper forms an industrial contribution with results important for marketing but also is scientific contribution because it introduces one new method of scoring TV channels based on previous measurements in their audience. We also developed IPTV Statistics model and described results from this research for a new statistics model. This paper is the continuous of previously published contributions from this area

Multi-dimensional clustering in user profiling

User profiling has attracted an enormous number of technological methods and applications. With the increasing amount of products and services, user profiling has created opportunities to catch the attention of the user as well as achieving high user satisfaction. To provide the user what she/he wants, when and how, depends largely on understanding them. The user profile is the representation of the user and holds the information about the user. These profiles are the outcome of the user profiling. Personalization is the adaptation of the services to meet the user’s needs and expectations. Therefore, the knowledge about the user leads to a personalized user experience. In user profiling applications the major challenge is to build and handle user profiles. In the literature there are two main user profiling methods, collaborative and the content-based. Apart from these traditional profiling methods, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, the profiling, achieved through these works, is lacking in terms of accuracy. This is because, all information within the profile has the same influence during the profiling even though some are irrelevant user information. In this thesis, a primary aim is to provide an insight into the concept of user profiling. For this purpose a comprehensive background study of the literature was conducted and summarized in this thesis. Furthermore, existing user profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these algorithms for user profiling was examined. A number of classification and clustering algorithms, such as Bayesian Networks (BN) and Decision Trees (DTs) have been simulated using user profiles and their classification accuracy performances were evaluated. Additionally, a novel clustering algorithm for the user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed. The MDC is a modified version of the Instance Based Learner (IBL) algorithm. In IBL every feature has an equal effect on the classification regardless of their relevance. MDC differs from the IBL by assigning weights to feature values to distinguish the effect of the features on clustering. Existing feature weighing methods, for instance Cross Category Feature (CCF), has also been investigated. In this thesis, three feature value weighting methods have been proposed for the MDC. These methods are; MDC weight method by Cross Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC) and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of these weighted MDC algorithms have been tested and evaluated. Additional simulations were carried out with existing weighted and non-weighted IBL algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user profiling to improve personalized service provisioning in mobile environments. The experiments presented in this thesis were conducted by using user profile datasets that reflect the user’s personal information, preferences and interests. The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA (version 3.5.7) machine learning platform. WEKA serves as a workbench to work with a collection of popular learning schemes implemented in JAVA. In addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life scenario is implemented as a Java Mobile Application (Java ME) on NetBeans IDE 7.1. All simulation results were evaluated based on the error rate and accuracy

SUMMARIZATION AND VISUALIZATION OF DIGITAL CONVERSATIONS

Digital conversations are all around us: recorded meetings, television debates, instant messaging, blogs, and discussion forums. With this work, we present some solutions for the condensation and distillation of content from digital conversation based on advanced language technology. At the core of this technology we have argumentative analysis, which allow us to produce high-quality text summaries and intuitive graphical visualizations of conversational content enabling easier and faster access to digital conversations

The impact of technological amenities on customer experience in upscale hotels

Upscale Hotels operate in a highly competitive market and therefore place a strong emphasis on providing quality service and differentiation through the latest technological amenities. Nowadays, hotel companies are trying to follow the customers’ desires in order to offer a unique experience. However, given the multitude of available technologies on the market today, hoteliers have little understanding of their guests’ expectations and of which technological amenities will drive guest satisfaction. The literature review shows that not all the technological amenities implemented in hotels have been appreciated by guests. Since technological items change rapidly over time, the purpose of this study is to analyze the impact of current technologies and to assess the potential of the latest technologies on customer experience. This study employed a two-step approach. In the qualitative phase an analysis of Portuguese upscale hotel websites was made as well as two interviews with hotel managers. In the quantitative stage a questionnaire was developed for hotel guests, generating a sample of 310 valid responses. The results revealed that Internet access was the most important technology for both leisure and business travelers. The majority of respondents would like to add new technologies or change some of the existing ones for new technologies in order to improve their experience. The results also demonstrate that installing specific new technology can have a significant effect on enhancing guest experience.Os Hotéis de 4 e 5 estrelas operam num mercado bastante competitivo, portanto têm uma grande necessidade de fornecer um serviço de qualidade e com diferenciação através das inovações tecnológicas mais recentes. Hoje em dia, as empresas hoteleiras estão a tentar seguir as necessidades do consumidor com o objetivo de oferecer uma experiência única. Contudo, dada a grande variedade de tecnologias disponíveis atualmente no mercado, os proprietários dos hotéis têm alguma dificuldade em saber quais são as expectativas dos clientes, ou seja, quais as tecnologias que podem levar à sua satisfação. A revisão da literatura mostra que nem todas as tecnologias implementadas pelos hotéis têm sido apreciadas pelos hóspedes. Como os itens tecnológicos mudam com o tempo é importante fazer este estudo que tem como objetivo analisar o impacto das tecnologias atuais bem como avaliar o potencial das mais recentes tecnologias na experiência do consumidor. A metodologia adotada para este estudo está dividida em duas fases. Na fase qualitativa foi feita uma análise a alguns websites de hotéis em Portugal, bem como duas entrevistas a gestores de hotéis. Na etapa quantitativa foi desenvolvido um questionário para os hóspedes e foi obtida um amostra de 310 respostas. Os resultados revelaram que o acesso à internet é a tecnologia mais importante tanto para os hóspedes que viajam em lazer como em negócios. A maioria da amostra gostaria de adicionar novas tecnologias ou mudar algumas das que estão disponíveis atualmente por novas para melhorar a sua experiência. Os resultados demonstraram também que a instalação de novas tecnologias específicas pode ter um efeito significativo na melhoria da experiência do cliente

The Importance of Context When Recommending TV Content: Dataset and Algorithms

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions

Entwicklungen im Web 2.0 aus technischer, ökonomischer und sozialer Sicht

Inhalt: I User-Generated Content 1 Tagging vs. Ontologies 3 David Stadler; Semantic Web vs. Web 2.0 13 Armin Stein; User-Generated Advertising 23 Burkhard Weiß; Qualitätsaspekte im User-Generated Content 39 Jens Feldkamp; Data Mining im User-Generated Content 51 Nico Albrecht; II Business-Aspekte 59 AdSense, verwandte Geschäftsmodelle und ihre Long-Tail-Effekte 61 Michael Räckers; SaaS-Geschäftsmodelle im Web 2.0 73 Sebastian Hallek; Monetisierung großer Datenmengen 85 Jan Lammers; Second Life 97 Gereon Strauch; Sicherheit und Vertrauen im Wandel vom Read zum Read/WriteWeb 115 Gunnar Thies; III Suchen und Sozialisieren 127 Universelles Suchen im Web Eine technische, ökonomische und soziale Betrachtung 129 Sebastian Herwig; Spezialisiertes Suchen im Web 141 Felix Müller-Wienbergen; Personalisierte Suche 153 Milan Karow; Blogging vs. Knowledge Management Wie Blogs zu gutem Wissensmanagement in Organisationen beitragen können 167 Daniel Beverungen; IV Technische Aspekte 179 Akamaiisierung von Applikationen 181 Ingo Düppe; IPTV 189 Philipp Bergener; Die Infrastruktur von Suchmaschinen am Fallbeispiel Google 197 Philipp Ciechanowicz; Amazon-Webservices Eine Betrachtung technischer, ökonomischer und sozialer Aspekte 207 Oliver Müller; P2P und VoIP 221 Christian Hermanns --

Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

Smart city environments, when applied to healthcare, improve the quality of people\u27s lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians\u27 daily work. Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services. The data produced by smart devices are accurate and reliable for predictive and decision-making contexts. This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring. To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases. The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms. The methodology used to carry out the study followed three steps. First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies. The results demonstrated the feasibility of the proposal. The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values