Durch die Vielzahl von heterogenen Informationsquellen sehen sich viele Nutzer einer kaum überschaubaren Informationsflut gegenüber. Aus diesem Grund werden durch diese Arbeit die gängigen Nachrichtenformate analysiert und der aktuelle Stand der Technik im Bereich der Nachrichtenaggregatoren dargelegt. Dabei werden diese Analysen immer mit Blick auf die Möglichkeiten einer personalisierten Filterung der Inhalte durchgeführt. Anschließend wird eine im Rahmen dieser Arbeit entstandene Infrastruktur für die Aggregation, personalisierte Filterung und kollaborative Empfehlung von Inhalten aus heterogenen Nachrichtenquellen vorgestellt. Dabei wird detailiert auf die zu Grunde liegenden Konzepte eingegangen und deren praktische Umsetzung beschrieben.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / A recommender system is a computational mechanism for information filtering, where users provide recommendations (in the form of ratings or selecting items) as inputs, which the system then aggregates and directs to appropriate recipients. With the advent of web based media and publicity methods, the age where standardized methods of publicity, sales, production and marketing strategies do not. As such, in many markets the users are given a wide range of products and information to choose which product they like, to find a way out of this recommender systems are used in a way similar to the live social scenario, that is a user tries to get reviews from friends before opting for a product in a similar way recommender system tries to be a friend who recommends the options. Most of the recommender systems currently developed solely accuracy driven, i.e., reducing the Mean Absolute Error (MAE) between the predictions of the recommender system and actual ratings of the user. This leads to various problems for recommender systems such as lack of diversity and freshness. Lack of diversity arises when the recommender system is overly focused on accuracy by recommending a set of items, in which all of the items are too similar to each other, because they are predicted to be liked by the user. Lack of freshness also arises with overly focusing on accuracy but as a limitation on the set of items recommended making it overly predictable. This thesis work is directed at addressing the issues of diversity, by developing an approach, where a threshold of accuracy (in terms of Mean Absolute Error in prediction) is maintained while trying to diversify the set of item recommendations. Here for the problem of diversity a combination of Attribute-based diversification and user preference based diversification is done. This approach is then evaluated using non-classical methods along with evaluating the base recommender algorithm to prove that diversification is indeed is possible with a mixture of collaborative and content based approach.

Computer technologies have revolutionised the processing of information and the search for knowledge. With the ever increasing computational power, it is becoming possible to tackle new data analysis applications as diverse as mining the Internet resources, analysing drugs effects on the organism or assisting wardens with autonomous video detection techniques. Fundamentally, the principle of any data analysis task is to fit a model which encodes well the dependencies (or patterns) present in the data. However, the difficulty is precisely to define such proper model when data are noisy, dependencies are highly stochastic and there is no simple physical rule to represent them. The aim of this work is to discuss the principles, the advantages and weaknesses of the probabilistic modelling framework for data analysis. The main idea of the framework is to model dispersion of data as well as uncertainty about the model itself by probability distributions. Three data analysis tasks are presented and for each of them the discussion is based on experimental results from real datasets. The first task considers the problem of linear subspaces identification. We show how one can replace a Gaussian noise model by a Student-t noise to make the identification more robust to atypical samples and still keep the learning procedure simple. The second task is about regression applied more specifically to near-infrared spectroscopy datasets. We show how spectra should be pre-processed before entering the regression model. We then analyse the validity of the Bayesian model selection principle for this application (and in particular within the Gaussian Process formulation) and compare this principle to the resampling selection scheme. The final task considered is Collaborative Filtering which is related to applications such as recommendation for e-commerce and text mining. This task is illustrative of the way how intuitive considerations can guide the design of the model and the choice of the probability distributions appearing in it. We compare the intuitive approach with a simpler matrix factorisation approach.

Information-seekers use a variety of information stores including electronic systems and the physical world experience of their community. Within electronic systems, information-seekers often report feelings of being lost and suffering from information overload. However, in the physical world they tend not to report the same negative feelings. This work draws on existing research including Collaborative Filtering, Recommender Systems and Social Navigation and reports on a new observational study of information-seeking behaviours. From the combined findings of the research and the observational study, a set of design considerations for the creation of a new electronic interface is proposed. Two new interfaces, the second built from the recommendations of the first, and a supporting methodology are created using the proposed design considerations. The second interface, the Collaborative Index, is shown to allow physical world behaviours to be used in the electronic world and it is argued that this has resulted in an alternative and preferred access route to information. This preferred route is a product of information-seekers' interactions 'within the machine' and maintains the integrity of the source information and navigational structures. The methodology used to support the Collaborative Index provides information managers with an understanding of the information-seekers' needs and an insight into their behaviours. It is argued that the combination of the Collaborative Index and its supporting methodology has provided the capability for information-seekers and information managers to 'enter into the machine', producing benefits for both groups.

The aim of this work is to explore recommender systems for prediction user's future film ratings according to their previous ratings. Author will describe available algorithms and compare their results with his own algorithm. The goal is to find algorithm with the highest prediction accuracy and find the most important parameters for a good predictions.

This report describes a distributed approach to social filtering based on the agent metaphor. Firstly, previous approaches are described, such as cognitive filtering and social filtering. Then a couple of previously implemented systems are presented and then a new system design is proposed. The main goal is to give the requirements and design of an agent-based system that recommends web-documents. The presented approach combines cognitive and social filtering to get the advantages from both techniques. Finally, a prototype implementation called WebCondor is described and results of testing the system are reported and discussed.

[pt] A recomendação de itens a partir do feedback implícito dos usuários consiste em identificar padrões no interesse dos usuários por estes itens a partir de ações dos usuários, tais como cliques, interações ou o consumo de conteúdos específicos. Isso, de forma a prover sugestões personalizadas que se adéquem ao gosto destes usuários. Nesta dissertação, avaliamos a performance de alguns modelos de fatoração matricial otimizados para a tarefa de recomendação a partir de dados implícitos no consumo das ofertas de vídeos da Globo.com. Propusemos tratar estes dados de consumo como indicativos de intenção de um usuário em assistir um vídeo. Além disso, avaliamos como os vieses únicos dos usuários e vídeos, e sua variação temporal impactam o resultado das recomendações. Também sugerimos a utilização de um modelo de fatoração incremental otimizado para este problema, que escala linearmente com o tamanho da entrada, isto é, com os dados de visualizações e quantidade de variáveis latentes. Na tarefa de prever a intenção dos usuários em consumir um conteúdo novo, nosso melhor modelo de fatoração apresenta um RMSE de 0,0524 usando o viés de usuários e vídeos, assim como sua variação temporal. / [en] Item recommendation from implicit feedback datasets consists of passively tracking different sorts of user behavior, such as purchase history, watching habits and browsing activities in order to improve customer experience through providing personalized recommendations that fits into users taste. In this work we evaluate the performance of different matrix factorization models tailored for the recommendation task for the implicit feedback dataset extracted from Globo.com s video site s access logs. We propose treating the data as indication of a positive preference from a user regarding the video watched. Besides that we evaluated the impact of effects associated with either users or items, known as biases or intercepts, independent of any interactions and its time changing behavior throughout the life span of the data in the result of recommendations. We also suggest a scalable and incremental procedure, which scales linearly with the input data size. In trying to predict the intention of the users for consuming new videos our best factorization models achieves a RMSE of 0,0524 using user s and video s bias as well as its temporal dynamics.

Fler och fler spel öppnas upp för användargenererat innehåll, vilket ofta resulterar i större mängder material än vad en ensam spelare kan utnyttja. Den unika spelare vill ta del av det som passar just dennes smak.Studien genomfördes med designforskning som metodval och med hjälp av denna metod skapades en artefakt. Med hjälp av den utvecklade artefakten, ett plattformspel som är både enkelt att förstå och spela, kunde en datamängd samlas in ifrån olika spelare. Data byggdes upp av att användarna efter varje slutförd nivå, explicit fick lämna sitt betyg på nivån i en skala mellan 1 och 5.Genom att introducera collaborative filtering och där låta användarens tidigare betyg jämföras med övriga användare kan en predicering av kommande betyg ges. Vid jämförelser av olika collaborative filtering-algoritmer kunde den mest lämpliga upptäckas och senare även användas.Resultaten visar att mer precisa uppskattningar av kommande betyg kan göras av collaborative filteringen än genom att använda nivåns medelbetyg och resultaten leder därför till slutsatsen att collaborative filtering kan ge skräddarsydda spelupplevelser för en unik användare och således förhöja dennes spelupplevelse. / Program: Systemarkitekturutbildningen

As primeiras mídias sociais da internet surgiram há pouco mais de duas décadas, segunda metade dos anos 90. Em comparação com a evolução humana, isso seria algo como um milésimo de segundo de sua existência. Neste período, vários estudos procuram entender o comportamento e o agrupamento dos seres humanos nesta nova forma de comunicação. Teorias sobre formas de analisar as pessoas neste meio e como elas se agrupam e criam novos modos de comunicação e propagação de suas ideias florescem e iluminam este desconhecido caminho a ser criado e percorrido. Os métodos de identificação do comportamento humano criados antes das mídias sociais ganham uma nova forma de serem utilizados. Estudos sobre o \"eu\" (Belk, 1988), tribalismo (Cova, B., 1997), etnografia (Danzig, 1985), netnografia (Kozinets, 1998) e filtragem colaborativa (Golberg, Nichols, Oki e Terry, 1992) entram em cena para colocar uma luz no estudo das relações humanas no mundo digital. A internet revolucionou o modo de as pessoas interagirem e a evolução constante da tecnologia vem incessantemente gerando profundas implicações para o marketing. A rede mundial passou a ser um canal global pelo qual as empresas podem divulgar e vender seus produtos. No entanto, mesmo oferecendo um enorme potencial para as empresas, a internet aumentou a complexidade de identificar os clientes. Os usuários presentes nas mídias sociais estão menos interessados nos produtos e valorizam mais as identidades e os laços sociais gerados em torno de seus assuntos de interesse. Estas tribos eletrônicas ultrapassam as fronteiras geográficas e independem de raça, sexo e aspectos culturais de seus integrantes. Este trabalho apresenta um método para identificar tribos nas mídias sociais. O método foi aplicado na identificação da tribo de MMA (MixedMartialArts, em tradução livre, Artes Marciais Mistas) no Twitter. A validação foi realizada usando a plataforma de anúncios do Twitter, enviando durante 72 horas uma publicidade para mais de 600 mil usuários, divididos em grupo de controle e segmentações do Twitter e do método proposto DNA. O estudo comparou os resultados obtidos pelo método proposto DNA com os resultados do grupo de controle e da segmentação realizada pelo Twitter. Os resultados obtidos apontaram o aumento de interações dos usuários identificados como pertencentes a tribo de MMA, validando o método. / The first Internet social media emerged just over two decades ago, at the second half of 90\'s. Compared to human evolution, this would be something like a millisecond of its existence. In this period, several studies try to understand the behavior and grouping of human beings in this new form of communication. Theories about ways of analyzing people in this environment and how they group themselves and create new ways of communication and propagation their ideas flourish and illuminate this unknown pathway to be created and traveled. Methods of identifying human behavior created before social media receive a new way of being used. Studies on the \"self\" (Belk, 1988), tribalism (Cova, B., 1997), ethnography (Danzig, 1985), netnography (Kozinets, 1998) and collaborative filtering (Golberg, Nichols, Oki and Terry, 1992) come on the scene to shed light on the study of human relations in the digital world. The Internet has revolutionized people\'s way of interacting and the constant evolution of technology generates profound implications for the marketing. The worldwide network has become a global channel through which companies can disclose and sell their products. However, while offering tremendous potential to businesses, the Internet has increased the complexity of identifying customers. Users present in social media are less interested in products and value more the identities and social ties generated around their subjects of interest. These electronic tribes transcend the geographical borders and are independent of race, sex and cultural aspects of its members. This paper presents a method to identify tribes in social media. The method was applied in the identification of the MMA (Mixed Martial Arts) tribe on Twitter. The validation was done using the Twitter ads platform, sending 72 hours of advertisement for more than 600 thousand users, divided in control group and segmentations of Twitter and the proposed method. The study compared the results obtained by the proposed method with that of the control group and the segmentation created by Twitter. The obtained results pointed out the increase of interactions of the users identified as belonging to the MMA tribe validating the method.

Recommender systems emerged in the mid '90s with the objective of helping users select items or products most suited for them. Whether it is Facebook recommending people you might know, Spotify recommending songs you might like or Youtube recommending videos you might want to watch, recommender systems can now be found in every corner of the internet. In order to handle the immense increase of data online, the development of sophisticated recommender systems is crucial for filtering out information, enhancing web services by tailoring them according to the preferences of the user. This thesis aims to improve the accuracy of recommendations produced by a classical collaborative filtering recommender system by utilizing temporal properties, more precisely the date on which an item was rated by a user. Three different time-weighted implementations are presented and evaluated: time-weighted prediction approach, time-weighted similarity approach and our proposed approach, weighting the mean rating of a user on time. The different approaches are evaluated using the well known MovieLens 100k dataset. Results show that it is possible to slightly increase the accuracy of recommendations by utilizing temporal properties.