Effect of Empowerment and Compensation on Performance of Honorary Employees Mediated by Organizational Commitments

The purpose of this study to analyze the influence of empowerment and compensation on employee performance is mediated by organizational commitment. The study was conducted by distributing questionnaires to a sample of 100 honorary employees in regional organizations in Denpasar using the proportionate random sampling method. The analytical method used is Partial Least Square (PLS) analysis. The results of PLS ​​analysis show that empowerment, compensation, and organizational commitment have a positive and significant direct effect on employee performance. Finally, organizational commitment has a positive and significant effect on mediating empowerment and compensation for employee performance. The results of this study imply that empowerment and compensation are important factors in improving employee performance. In addition, the mediating role of organizational commitment can also contribute to improving performance. The management of each regional apparatus organization in Denpasar City needs to pay attention to these matters so that organizational goals can be achieved

A methodology of personalized recommendation system on mobile device for digital television viewers

With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW. These annotations will be used to find other programs that are similar by using CBSA. Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer individually

Jointly integrating current context and social influence for improving recommendation

La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les utilisateurs. D'autre part, la résolution de problèmes classiques de recommandation (par exemple, aucun programme de télévision vu par un nouvel utilisateur connu sous le nom du problème de démarrage froid et pas assez d'items co-évalués par d'autres utilisateurs ayant des préférences similaires, connu sous le nom du problème de manque de donnes) est d'importance significative puisque sont attaqués par plusieurs travaux. Dans notre travail de thèse, nous proposons un modèle probabiliste qui permet exploiter conjointement les informations contextuelles actuelles et l'influence sociale afin d'améliorer la recommandation des items. En particulier, le modèle probabiliste vise prédire la pertinence de contenu pour un utilisateur en fonction de son contexte actuel et de son influence sociale. Nous avons considérer plusieurs éléments du contexte actuel des utilisateurs tels que l'occasion, le jour de la semaine, la localisation et la météo. Nous avons utilisé la technique de lissage Laplace afin d'éviter les fortes probabilités. D'autre part, nous supposons que l'information provenant des relations sociales a une influence potentielle sur les préférences des utilisateurs. Ainsi, nous supposons que l'influence sociale dépend non seulement des évaluations des amis mais aussi de la similarité sociale entre les utilisateurs. Les similarités sociales utilisateur-ami peuvent être établies en fonction des interactions sociales entre les utilisateurs et leurs amis (par exemple les recommandations, les tags, les commentaires). Nous proposons alors de prendre en compte l'influence sociale en fonction de la mesure de similarité utilisateur-ami afin d'estimer les préférences des utilisateurs. Nous avons mené une série d'expérimentations en utilisant un ensemble de donnes réelles issues de la plateforme de TV sociale Pinhole. Cet ensemble de donnes inclut les historiques d'accès des utilisateurs-vidéos et les réseaux sociaux des téléspectateurs. En outre, nous collectons des informations contextuelles pour chaque historique d'accès utilisateur-vidéo saisi par le système de formulaire plat. Le système de la plateforme capture et enregistre les dernières informations contextuelles auxquelles le spectateur est confronté en regardant une telle vidéo.Dans notre évaluation, nous adoptons le filtrage collaboratif axé sur le temps, le profil dépendant du temps et la factorisation de la matrice axe sur le réseau social comme tant des modèles de référence. L'évaluation a port sur deux tâches de recommandation. La première consiste sélectionner une liste trie de vidéos. La seconde est la tâche de prédiction de la cote vidéo. Nous avons évalué l'impact de chaque élément du contexte de visualisation dans la performance de prédiction. Nous testons ainsi la capacité de notre modèle résoudre le problème de manque de données et le problème de recommandation de démarrage froid du téléspectateur. Les résultats expérimentaux démontrent que notre modèle surpasse les approches de l'état de l'art fondes sur le facteur temps et sur les réseaux sociaux. Dans les tests des problèmes de manque de donnes et de démarrage froid, notre modèle renvoie des prédictions cohérentes différentes valeurs de manque de données.Due to the diversity of alternative contents to choose and the change of users' preferences, real-time prediction of users' preferences in certain users' circumstances becomes increasingly hard for recommender systems. However, most existing context-aware approaches use only current time and location separately, and ignore other contextual information on which users' preferences may undoubtedly depend (e.g. weather, occasion). Furthermore, they fail to jointly consider these contextual information with social interactions between users. On the other hand, solving classic recommender problems (e.g. no seen items by a new user known as cold start problem, and no enough co-rated items with other users with similar preference as sparsity problem) is of significance importance since it is drawn by several works. In our thesis work, we propose a context-based approach that leverages jointly current contextual information and social influence in order to improve items recommendation. In particular, we propose a probabilistic model that aims to predict the relevance of items in respect with the user's current context. We considered several current context elements such as time, location, occasion, week day, location and weather. In order to avoid strong probabilities which leads to sparsity problem, we used Laplace smoothing technique. On the other hand, we argue that information from social relationships has potential influence on users' preferences. Thus, we assume that social influence depends not only on friends' ratings but also on social similarity between users. We proposed a social-based model that estimates the relevance of an item in respect with the social influence around the user on the relevance of this item. The user-friend social similarity information may be established based on social interactions between users and their friends (e.g. recommendations, tags, comments). Therefore, we argue that social similarity could be integrated using a similarity measure. Social influence is then jointly integrated based on user-friend similarity measure in order to estimate users' preferences. We conducted a comprehensive effectiveness evaluation on real dataset crawled from Pinhole social TV platform. This dataset includes viewer-video accessing history and viewers' friendship networks. In addition, we collected contextual information for each viewer-video accessing history captured by the plat form system. The platform system captures and records the last contextual information to which the viewer is faced while watching such a video. In our evaluation, we adopt Time-aware Collaborative Filtering, Time-Dependent Profile and Social Network-aware Matrix Factorization as baseline models. The evaluation focused on two recommendation tasks. The first one is the video list recommendation task and the second one is video rating prediction task. We evaluated the impact of each viewing context element in prediction performance. We tested the ability of our model to solve data sparsity and viewer cold start recommendation problems. The experimental results highlighted the effectiveness of our model compared to the considered baselines. Experimental results demonstrate that our approach outperforms time-aware and social network-based approaches. In the sparsity and cold start tests, our approach returns consistently accurate predictions at different values of data sparsity

An architecture for evolving the electronic programme guide for online viewing

Watching television and video content is changing towards online viewing due to the proliferation of content providers and the prevalence of high speed broadband. This trend is coupled to an acceleration in the move to watching content using non-traditional viewing devices such as laptops, tablets and smart phones. This, in turn, poses a problem for the viewer in that it is becoming increasingly difficult to locate those programmes of interest across such a broad range of providers. In this thesis, an architecture of a generic cloud-based Electronic Programme Guide (EPG) system has been developed to meet this challenge. The key feature of this architecture is the way in which it can access content from all of the available online content providers and be personalized depending on the viewer’s preferences and interests, viewing device, internet connection speed and their social network interactions. Fundamental to its operation is the translation of programme metadata adopted by each provider into a unified format that is used within the core system. This approach ensures that the architecture is extensible, being able to accommodate any new online content provider through the addition of a small tailored search agent module. The EPG system takes the programme as its core focus and provides a single list of recommendations to each user regardless of their origins. A prototype has been developed in order to validate the proposed system and evaluate its operation. Results have been obtained through a series of user trials to assess the system’s effectiveness in being able to extract content from several sources and to produce a list of recommendations which match the user’s preferences and context. Results show that the EPG is able to offer users a single interface to online television and video content providers and that its integration with social networks ensures that the recommendation process is able to match or exceed the published results from comparable, but more constrained, systems

Recomendação personalizada dinâmica de informação sobre serviços públicos e sociais na iTV para seniores : um estudo de caso

A difusão e o acesso adequado à informação sobre Serviços de Interesse Geral são direitos constitucionais dos cidadãos e integram fatores determinantes na estruturação de uma sociedade mais igualitária e baseada na democratização do conhecimento. No entanto, não obstante a crescente quantidade de informação disponível e a evolução das TIC, verifica-se que o cidadão sénior, muitas vezes caraterizado pelos seus baixos níveis de literacia digital e infoinclusão, tem frequentemente dificuldade em aceder a informações sobre políticas e serviços públicos e sociais dos quais pode beneficiar. Com necessidades informacionais específicas e cada vez mais tempo livre decorrente da reforma, os seniores tendem a utilizar a TV como meio primordial de informação e entretenimento. Deste modo, beneficiando da familiaridade deste público com a TV, muitas soluções tecnológicas inovadoras têm-se baseado neste dispositivo. No entanto, apenas conceber e empregar recursos tecnologicamente avançados não é suficiente. É, sim, preciso elaborar soluções personalizadas, que possam melhor adaptar-se às preferências e limitações deste segmento populacional. Neste caso concreto, tal trata-se de identificar qual a informação mais adequada a ser enviada a cada sénior. Por exemplo, informações sobre campanhas de saúde e descontos em taxas moderadoras devem ser enviadas conforme as preferências e o contexto (e.g. localização) do utilizador. Este trabalho propõe uma estratégia de personalização para a entrega de conteúdos informativos sobre Serviços de Interesse Geral, em um ambiente televisivo, para a população sénior. Para tal, este trabalho tem por objetivo alavancar a exibição de vídeos informativos através da integração de um Sistema de Recomendação Sensível ao Contexto (CARS). A investigação dividiu-se em três etapas distintas, numa abordagem de design participativo, de modo que o CARS seja adequado às especificidades deste segmento populacional, considerando as opiniões e indicações de vários seniores em todas as fases do estudo. Na primeira etapa, são caracterizados os dados do trinómio [Item x Utilizador x Contexto]. Esta etapa decorreu com colaboração de especialistas nas áreas de gerontologia, serviços públicos e TV Interativa, bem como com a colaboração de seniores recrutados no âmbito do projeto +TV4E, a partir da aplicação de entrevistas, focus groups e testes guiados. Na segunda etapa, é proposto o CARS de acordo com o Modelo de dados e o esquema de interação obtidos a partir dos resultados provenientes da etapa anterior. Um algoritmo de recomendação híbrido é proposto para gerar as recomendações. Por fim, na terceira e última etapa, foi desenvolvido um protótipo, integrado no projeto +TV4E, de modo a validar o CARS, em ambiente doméstico, por um período de duas semanas e com o apoio de 21 seniores residentes no distrito de Aveiro. A análise dos resultados, a partir dos registos de utilização do protótipo e de entrevistas, corroboram a utilidade e adequabilidade da estratégia de personalização proposta.The dissemination and adequate access to information about Services of General Interest are constitutional rights of the citizens and play a major role in structuring a more egalitarian society based on the democratization of knowledge. However, despite the increasing amount of information available and the evolution of information and communication technologies (ICT), senior citizens, often characterized by lower levels of digital literacy and info-inclusion, often struggle to access information about policies and services that they can benefit from. With specific informational needs and free time due to retirement, seniors tend to use TV as a primary mean of information and entertainment. In this way, benefiting from the familiarity of these citizens with the TV, many innovative technological solutions have been leveraged this device. However, solely designing and employing technologically advanced features is not enough. It is necessary to develop personalized solutions to better adapt to seniors’ preferences and limitations. In this case, this concerns identifying which information is more appropriate to be provided for each senior. For example, information on health campaigns and social tariffs discounts should be tailored according to the user’s specific preferences and contextual factors (e.g. location and dates). That said, this research proposes a personalization strategy for the delivery of highvalued informative contents about Services of General Interest for the senior population. To this end, this work aims to leverage the informative videos exhibition through the integration of a Context-Aware Recommender System (CARS). The investigation was divided into three distinct phases, in a participatory design approach, so that the CARS is adequate to the specifics of this population segment, considering seniors’ opinions and indications in all phases of the study. In the first phase, data of the trinomial [Item x User x Context] is characterized. In addition, this phase was carried out with the collaboration of specialists in the areas of gerontology, public services, interactive TV and software engineering, as well as the collaboration of seniors recruited under the + TV4E project, through the application of interviews, focus groups and guided tests. In the second phase, the CARS is proposed according to the Data Model and the interaction scheme obtained from the results of the previous phase. A hybrid filtering algorithm is proposed to generate the recommendations. Finally, in the third and last phase, a prototype was developed and integrated in the scope of + TV4E project, in order to validate the CARS, in a domestic environment, for a period of two weeks, and with the support of 21 senior residents in the district of Aveiro. The analysis of the results, based on user interactions and interviews, corroborate the usefulness and appropriateness of the personalization strategy proposed by CARS.Programa Doutoral em Informação e Comunicação em Plataformas Digitai