Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created and continue to develop the TopicShop system to address this need. TopicShop includes (1) a web crawler/analyzer that discovers relevant web sites and builds site profiles, and (2) user interfaces for information workspaces. We conducted an empirical pilot study comparing user performance with TopicShop vs. Yahooï . Results from this study were used to improve the design of TopicShop. A number of key design changes were incorporated into a second version of TopicShop based on results and user comments of the pilot study including (1) the tasks of evaluation and organization are treated as integral instead of separable, (2) spatial organization is important to users and must be well supported in the interface, and (3) distinct user and global datasets help users deal with the large quantity of information available on the web. A full empirical study using the second iteration of TopicShop covered more areas of the World Wide Web and validated results from the pilot study. Across the two studies, TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 81% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provide -- in particular, the number of pages on a site and the number of other sites that link to it -- were the key to these results, as users exploited them to identify the most promising sites quickly and easily. We also evaluated a number of link- and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 (the common information retrieval metric indicating the percentage of high quality items selected that are actually high quality) is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 32% of all documents were of high quality. Surprisingly, a simple content-based metric, which ranked documents by the total number of pages on their containing site, performed nearly as well. These studies give insight into users' needs for the task of topic management, and provide empirical evidence of the effectiveness of task-specific interfaces (such as TopicShop) for managing topical collections. / Ph. D.

Sistemas de recomendação estão se tornando ferramentas indispensáveis para diversos websites, que buscam oferecer ao seu usuário uma experiência personalizada e simplificada, e sua adoção se deve principalmente devido ao grande volume de dados disponíveis, advindos de diferentes fontes e contendo informações diversificadas, aumentando a necessidade e a complexidade de se extrair valor desses dados. Com o surgimento de redes sociais online os usuários passaram a expressar seus gostos e preferências além de estabelecer relações com outros usuários, podendo estes serem seus amigos, parentes, ídolos, etc. Estas possibilidades encontradas em redes sociais motivou o presente trabalho a interpretar a comunidade científica como uma rede social, utilizando relações de coautoria, colaboração em projetos, orientações, além de citações de trabalhos e, consequentemente, citações aos respectivos autores. O objetivo deste projeto foi propor um sistema de recomendação de trabalhos científicos combinando informações sociais e informações bibliométricas, no que diz respeito a artigos citados em publicações, caraterizando-se como um facilitador para auxiliar os pesquisadores a responderem perguntas como: Quais artigos interessantes da minha área eu ainda não tenho conhecimento? e Quais artigos podem auxiliar em trabalhos que tenho em desenvolvimento? Para atingir o objetivo proposto foram desenvolvidas duas abordagens de recomendação. A primeira abordagem teve como premissa que o tempo em que as relações entre os autores foi estabelecida é determinante para selecionar os autores mais próximos (ou similares), ou seja, as relações mais recentes tendem a ser mais relevantes que as relações mais antigas. Já a segunda técnica combinou o resultados das diferentes técnicas implementadas (tanto a proposta quanto técnicas da literatura correlata) para gerar novas recomendações de maneira híbrida. Os resultados mostraram que a solução baseada no tempo apresentou resultados superiores às estratégias correlatas quando se possui mais informações sobre o autor, ou seja, autores que possuem diversas relações de coautoria e um conjunto de artigos citados elevado tendem a obter resultados melhores quando comparados aos autores que possuem poucas relações e citaram poucos artigos. Já a solução híbrida, que combina os resultados dos diversos recomendadores, apresentou uma cobertura de recomendações superior às demais, pelo fato de combinar os pontos fortes de cada uma das técnicas, encontrando recomendações relevantes no conjunto de testes em mais de 57% dos casos / Recommender systems are becoming indispensable tools on websites, in order to offer a simplified and personalized experience to their users, and its adoption is due to the fact that the volume of data available has increased and also comes from different sources with different types of information. Thus, it is challenge and necessary tools for helping to extract more valuable information from these data. The arise of online social networks allowed users to express their tastes and preferences and establish relationships with other users, such as friends, relatives, idols, etc. Those possibilities found in social networks motivated this work to interpret the scientific community as a social network, providing the ability to use co-authorship relations, collaboration in projects, tutoring relations, as well as paper citations and thus citations from their authors. The goal of this project was to propose a papers recommender system combining social and bibliometric information, regarding cited articles on published papers, being characterized as a facilitator to help researchers to answer questions such as: \"What interesting articles in my area I still have no knowledge of?\" and \"Which articles can assist in the project I am developing?\". The first algorithm proposed used the time when the coauthorship relations among authors were established as a determining parameter to choose which authors are more similar, meaning that relations established in recent time are more relevant than those that are older. The second algorithm combines the results from different implemented algorithms to determine which would be the ideal weight of each algorithm on the recommendation result, using a linear regression on the recommendations scores. The results showed that the time based solution achieved a better performance for the authors with higher amount of information available, i.e., if the author has many coauthorship relations and cited many papers, the results are better when compared with authors that does not have many relations and cited articles. On the other hand, the hybrid solution which combines the results from different recommendations approaches presented a higher coverage compared with others, due to the fact that it combines the strengths of each one of the algorithms, finding recommendation for users on 57% of the cases.

Sistemas de recomendação estão se tornando ferramentas indispensáveis para diversos websites, que buscam oferecer ao seu usuário uma experiência personalizada e simplificada, e sua adoção se deve principalmente devido ao grande volume de dados disponíveis, advindos de diferentes fontes e contendo informações diversificadas, aumentando a necessidade e a complexidade de se extrair valor desses dados. Com o surgimento de redes sociais online os usuários passaram a expressar seus gostos e preferências além de estabelecer relações com outros usuários, podendo estes serem seus amigos, parentes, ídolos, etc. Estas possibilidades encontradas em redes sociais motivou o presente trabalho a interpretar a comunidade científica como uma rede social, utilizando relações de coautoria, colaboração em projetos, orientações, além de citações de trabalhos e, consequentemente, citações aos respectivos autores. O objetivo deste projeto foi propor um sistema de recomendação de trabalhos científicos combinando informações sociais e informações bibliométricas, no que diz respeito a artigos citados em publicações, caraterizando-se como um facilitador para auxiliar os pesquisadores a responderem perguntas como: Quais artigos interessantes da minha área eu ainda não tenho conhecimento? e Quais artigos podem auxiliar em trabalhos que tenho em desenvolvimento? Para atingir o objetivo proposto foram desenvolvidas duas abordagens de recomendação. A primeira abordagem teve como premissa que o tempo em que as relações entre os autores foi estabelecida é determinante para selecionar os autores mais próximos (ou similares), ou seja, as relações mais recentes tendem a ser mais relevantes que as relações mais antigas. Já a segunda técnica combinou o resultados das diferentes técnicas implementadas (tanto a proposta quanto técnicas da literatura correlata) para gerar novas recomendações de maneira híbrida. Os resultados mostraram que a solução baseada no tempo apresentou resultados superiores às estratégias correlatas quando se possui mais informações sobre o autor, ou seja, autores que possuem diversas relações de coautoria e um conjunto de artigos citados elevado tendem a obter resultados melhores quando comparados aos autores que possuem poucas relações e citaram poucos artigos. Já a solução híbrida, que combina os resultados dos diversos recomendadores, apresentou uma cobertura de recomendações superior às demais, pelo fato de combinar os pontos fortes de cada uma das técnicas, encontrando recomendações relevantes no conjunto de testes em mais de 57% dos casos / Recommender systems are becoming indispensable tools on websites, in order to offer a simplified and personalized experience to their users, and its adoption is due to the fact that the volume of data available has increased and also comes from different sources with different types of information. Thus, it is challenge and necessary tools for helping to extract more valuable information from these data. The arise of online social networks allowed users to express their tastes and preferences and establish relationships with other users, such as friends, relatives, idols, etc. Those possibilities found in social networks motivated this work to interpret the scientific community as a social network, providing the ability to use co-authorship relations, collaboration in projects, tutoring relations, as well as paper citations and thus citations from their authors. The goal of this project was to propose a papers recommender system combining social and bibliometric information, regarding cited articles on published papers, being characterized as a facilitator to help researchers to answer questions such as: \"What interesting articles in my area I still have no knowledge of?\" and \"Which articles can assist in the project I am developing?\". The first algorithm proposed used the time when the coauthorship relations among authors were established as a determining parameter to choose which authors are more similar, meaning that relations established in recent time are more relevant than those that are older. The second algorithm combines the results from different implemented algorithms to determine which would be the ideal weight of each algorithm on the recommendation result, using a linear regression on the recommendations scores. The results showed that the time based solution achieved a better performance for the authors with higher amount of information available, i.e., if the author has many coauthorship relations and cited many papers, the results are better when compared with authors that does not have many relations and cited articles. On the other hand, the hybrid solution which combines the results from different recommendations approaches presented a higher coverage compared with others, due to the fact that it combines the strengths of each one of the algorithms, finding recommendation for users on 57% of the cases.

Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution formats. However, having more options also means that deciding what to watch becomes more difficult, and it is important that IPTV providers facilitate the process of finding interesting content so that the users find value in using their services. In this thesis, the author investigated how a user’s online social network can be used as a basis for facilitating the discovery of interesting movies in an IPTV environment. The study consisted of two parts, a theoretical and a practical. In the theoretical part, a literature study was carried out in order to obtain knowledge about different recommender system strategies. In addition to the literature study, a number of online social network platforms were identified and empirically studied in order to gain knowledge about what data is possible to gather from them, and how the data can be gathered. In the practical part, a prototype content discovery system, which made use of the gathered data, was designed and built. This was done in order to uncover difficulties that exist with implementing such a system. The study shows that, while it is is possible to gather data from different online social networks, not all of them offer data in a form that is easy to make use of in a content discovery system. Out of the investigated online social networks, Facebook was found to offer data that is the easiest to gather and make use of. The biggest obstacle, from a technical point of view, was found to be the matching of movie titles gathered from the online social network with the movie titles in the database of the IPTV service provider; one reason for this is that movies can have titles in different languages. / Internet Protocol television (IPTV) är ett sätt att leverera tv via Internet, vilket möjliggör tvåvägskommunikation mellan en operatör och dess användare. Genom att använda IPTV har användare friheten att välja vilket innehåll de vill konsumera och när de vill konsumera det. Användare har t.ex. möjlighet att titta på tv program efter att de har sänts på tv, och de kan komma åt innehåll som inte är en del av någon linjär tv-sändning, t.ex. filmer som är tillgängliga att hyra. Detta betyder att användare, genom att använda IPTV, kan få tillgång till mer videoinnhåll än vad som är möjligt med traditionella tv-distributionsformat. Att ha fler valmöjligheter innebär dock även att det blir svårare att bestämma sig för vad man ska titta på, och det är viktigt att IPTV-leverantörer underlättar processen att hitta intressant innehåll så att användarna finner värde i att använda deras tjänster. I detta exjobb undersökte författaren hur en användares sociala nätverk på Internet kan användas som grund för att underlätta upptäckandet av intressanta filmer i en IPTV miljö. Undersökningen bestod av två delar, en teoretisk och en praktisk. I den teoretiska delen genomfördes en litteraturstudie för att få kunskap om olika rekommendationssystemsstrategier. Utöver litteraturstudien identifierades ett antal sociala nätverk på Internet som studerades empiriskt för att få kunskap om vilken data som är möjlig att hämta in från dem och hur datan kan inhämtas. I den praktiska delen utformades och byggdes en prototyp av ett s.k. content discovery system (“system för att upptäcka innehåll”), som använde sig av den insamlade datan. Detta gjordes för att exponera svårigheter som finns med att implementera ett sådant system. Studien visar att, även om det är möjligt att samla in data från olika sociala nätverk på Internet så erbjuder inte alla data i en form som är lätt att använda i ett content discovery system. Av de undersökta sociala nätverkstjänsterna visade det sig att Facebook erbjuder data som är lättast att samla in och använda. Det största hindret, ur ett tekniskt perspektiv, visade sig vara matchningen av filmtitlar som inhämtats från den sociala nätverkstjänsten med filmtitlarna i IPTV-leverantörens databas; en anledning till detta är att filmer kan ha titlar på olika språk.