O avanço da tecnologia nos últimos anos permitiu a criação de Sistemas de Informação com acesso a grandes bases de dados, abrindo diversas possibilidades de aplicações. Tem-se como exemplo a Internet, onde uma enorme quantidade de dados é gerada e publicada a todo momento por usuários ao redor do mundo. Com isso, aos poucos foi surgindo a necessidade de métodos para filtrar o conteúdo disponível de forma a permitir que um usuário pudesse focar apenas nos seus interesses. Nesse contexto surgiram os Sistemas de Recomendação e as Redes Sociais, onde, mais recentemente, surgiram trabalhos que apresentam abordagens para o uso de Sistemas de Recomendação no contexto acadêmico, de forma a aumentar a produtividade de grupos de pesquisa. Também têm sido bastante exploradas formas de se utilizar informações temporais em Sistemas de Recomendação de maneira a melhorar as recomendações feitas. O presente trabalho propõe uma abordagem de recomendação de colaborações acadêmicas utilizando a técnica de Análise de Séries Temporais, buscando melhorar os resultados obtidos por trabalhos anteriores. Foi realizado um experimento offline para avaliar o desempenho da abordagem proposta em relação às abordagens anteriores e um estudo de usuários para fazer uma análise mais profunda com feedback de usuários. Foram utilizadas métricas conhecidas das áreas de Recuperação de Informação e Sistemas de Recomendação, mas alguns resultados se mostraram inferiores em comparação com as abordagens existentes; outros, porém, foram similares. Também foram utilizadas algumas métricas de avaliação focadas em Sistemas de Recomendação, e os resultados obtidos foram similares em todas as abordagens testadas. / The advance of technology in recent years made possible the creation of Information Systems with access to large databases, opening many applications possibilities. There’s the Internet, for example, where a vast amount of data is generated and published all the time by users around the world. In this sense, the need for methods to filter the available content to enable users to focus only on their interests slowly emerged. In this context, Recommender Systems and Social Networks appeared, where, recently, works reporting approaches to provide recommendations in the academic context appeared, increasing the productivity of research groups. New ways to employ temporal information in Recommender Systems to make better recommendations are also being explored. The present work proposes an approach to academic collaborations recommendation using Time Series Analysis, aiming to improve results reported on previous and current works. An offline experiment was done to evaluate the proposed approach in comparison with other works and a user study was done to make a deeper analysis from user feedback. Known metrics from the Information Retrieval and Recommender Systems fields were used, and in some cases the results obtained were lower compared to the current methods but similar in others. Some evaluation metrics from Recommender Systems were also used, and the results were similar to all approaches.

Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest. Recommender systems have been applied to many domains from music to e-commerce, movies to software services delivery and tourism to news by exploiting available information to predict and provide recommendations to end user. The suggestions generated by recommender systems tend to narrow down the list of items which a user may overlook due to the huge variety of similar items or users’ lack of experience in the particular domain of interest. While the performance of traditional recommender systems, which rely on relatively simpler information such as content and users’ filters, is widely accepted, their predictive capability perfomrs poorly when local context of the user and situated actions have significant role in the final decision. Therefore, acceptance and incorporation of context of the user as a significant feature and development of recommender systems utilising the premise becomes an active area of research requiring further investigation of the underlying algorithms and methodology. This thesis focuses on categorisation of contextual and non-contextual features within the domain of context-aware recommender system and their respective evaluation. Further, application of the Multilayer Perceptron Model (MLP) for generating predictions and ratings from the contextual and non-contextual features for contextual recommendations is presented with support from relevant literature and empirical evaluation. An evaluation of specifically employing artificial neural networks (ANNs) in the proposed methodology is also presented. The work emphasizes on both algorithms and methodology with three points of consideration: contextual features and ratings of particular items/movies are exploited in several representations to improve the accuracy of recommendation process using artificial neural networks (ANNs), context features are combined with user-features to further improve the accuracy of a context-aware recommender system and lastly, a combination of the item/movie features are investigated within the recommendation process. The proposed approach is evaluated on the LDOS-CoMoDa dataset and the results are compared with state-of-the-art approaches from relevant published literature.

O avanço da tecnologia nos últimos anos permitiu a criação de Sistemas de Informação com acesso a grandes bases de dados, abrindo diversas possibilidades de aplicações. Tem-se como exemplo a Internet, onde uma enorme quantidade de dados é gerada e publicada a todo momento por usuários ao redor do mundo. Com isso, aos poucos foi surgindo a necessidade de métodos para filtrar o conteúdo disponível de forma a permitir que um usuário pudesse focar apenas nos seus interesses. Nesse contexto surgiram os Sistemas de Recomendação e as Redes Sociais, onde, mais recentemente, surgiram trabalhos que apresentam abordagens para o uso de Sistemas de Recomendação no contexto acadêmico, de forma a aumentar a produtividade de grupos de pesquisa. Também têm sido bastante exploradas formas de se utilizar informações temporais em Sistemas de Recomendação de maneira a melhorar as recomendações feitas. O presente trabalho propõe uma abordagem de recomendação de colaborações acadêmicas utilizando a técnica de Análise de Séries Temporais, buscando melhorar os resultados obtidos por trabalhos anteriores. Foi realizado um experimento offline para avaliar o desempenho da abordagem proposta em relação às abordagens anteriores e um estudo de usuários para fazer uma análise mais profunda com feedback de usuários. Foram utilizadas métricas conhecidas das áreas de Recuperação de Informação e Sistemas de Recomendação, mas alguns resultados se mostraram inferiores em comparação com as abordagens existentes; outros, porém, foram similares. Também foram utilizadas algumas métricas de avaliação focadas em Sistemas de Recomendação, e os resultados obtidos foram similares em todas as abordagens testadas. / The advance of technology in recent years made possible the creation of Information Systems with access to large databases, opening many applications possibilities. There’s the Internet, for example, where a vast amount of data is generated and published all the time by users around the world. In this sense, the need for methods to filter the available content to enable users to focus only on their interests slowly emerged. In this context, Recommender Systems and Social Networks appeared, where, recently, works reporting approaches to provide recommendations in the academic context appeared, increasing the productivity of research groups. New ways to employ temporal information in Recommender Systems to make better recommendations are also being explored. The present work proposes an approach to academic collaborations recommendation using Time Series Analysis, aiming to improve results reported on previous and current works. An offline experiment was done to evaluate the proposed approach in comparison with other works and a user study was done to make a deeper analysis from user feedback. Known metrics from the Information Retrieval and Recommender Systems fields were used, and in some cases the results obtained were lower compared to the current methods but similar in others. Some evaluation metrics from Recommender Systems were also used, and the results were similar to all approaches.

O avanço da tecnologia nos últimos anos permitiu a criação de Sistemas de Informação com acesso a grandes bases de dados, abrindo diversas possibilidades de aplicações. Tem-se como exemplo a Internet, onde uma enorme quantidade de dados é gerada e publicada a todo momento por usuários ao redor do mundo. Com isso, aos poucos foi surgindo a necessidade de métodos para filtrar o conteúdo disponível de forma a permitir que um usuário pudesse focar apenas nos seus interesses. Nesse contexto surgiram os Sistemas de Recomendação e as Redes Sociais, onde, mais recentemente, surgiram trabalhos que apresentam abordagens para o uso de Sistemas de Recomendação no contexto acadêmico, de forma a aumentar a produtividade de grupos de pesquisa. Também têm sido bastante exploradas formas de se utilizar informações temporais em Sistemas de Recomendação de maneira a melhorar as recomendações feitas. O presente trabalho propõe uma abordagem de recomendação de colaborações acadêmicas utilizando a técnica de Análise de Séries Temporais, buscando melhorar os resultados obtidos por trabalhos anteriores. Foi realizado um experimento offline para avaliar o desempenho da abordagem proposta em relação às abordagens anteriores e um estudo de usuários para fazer uma análise mais profunda com feedback de usuários. Foram utilizadas métricas conhecidas das áreas de Recuperação de Informação e Sistemas de Recomendação, mas alguns resultados se mostraram inferiores em comparação com as abordagens existentes; outros, porém, foram similares. Também foram utilizadas algumas métricas de avaliação focadas em Sistemas de Recomendação, e os resultados obtidos foram similares em todas as abordagens testadas. / The advance of technology in recent years made possible the creation of Information Systems with access to large databases, opening many applications possibilities. There’s the Internet, for example, where a vast amount of data is generated and published all the time by users around the world. In this sense, the need for methods to filter the available content to enable users to focus only on their interests slowly emerged. In this context, Recommender Systems and Social Networks appeared, where, recently, works reporting approaches to provide recommendations in the academic context appeared, increasing the productivity of research groups. New ways to employ temporal information in Recommender Systems to make better recommendations are also being explored. The present work proposes an approach to academic collaborations recommendation using Time Series Analysis, aiming to improve results reported on previous and current works. An offline experiment was done to evaluate the proposed approach in comparison with other works and a user study was done to make a deeper analysis from user feedback. Known metrics from the Information Retrieval and Recommender Systems fields were used, and in some cases the results obtained were lower compared to the current methods but similar in others. Some evaluation metrics from Recommender Systems were also used, and the results were similar to all approaches.

<p> The dramatic improvement in information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. CourseNetworking is a next-generation LMS adopting machine learning to add personalization, gamification, and more dynamics to the system. This work tries to come up with two recommender systems that can help improve CourseNetworking services. The first one is a social recommender system helping CourseNetworking to track user interests and give more relevant recommendations. Recently, graph neural network (GNN) techniques have been employed in social recommender systems due to their high success in graph representation learning, including social network graphs. Despite the rapid advances in recommender systems performance, dealing with the dynamic property of the social network data is one of the key challenges that is remained to be addressed. In this research, a novel method is presented that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph by supplementing the graph with time span nodes that are used to define users long-term and short-term preferences over time. The second service that is proposed to add to Rumi services is a hashtag recommendation system that can help users label their posts quickly resulting in improved searchability of content. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This work investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation and recommends the one with the best performance to use in a hashtag recommender system. </p>

The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support systems like Business Intelligence (BI) platforms also face usability challenges as the amount of information available to knowledge workers grows. Remarkably, we observe that only a small part of personalization and recommendation techniques have been used in the context of data warehouses and analysis tools. Therefore, our work aims at exploring synergies of recommender systems and context-aware systems to develop personalization and recommendation scenarios suited in a BI environment. In response to this, we develop in our work an open and modular situation management platform using a graph-based situation model. Besides, dynamic aspects are crucial to deal with context data which is inherently time-dependent. We thus define two types of active components to enable dynamic maintenance of situation graphs, activation rules and operators. In response to events which can describe users' interactions, activation rules - defined using the event-condition-action framework - are evaluated thanks to queries on underlying graphs, to eventually trigger appropriate operators. These platform and framework allow us to develop and support various recommendation and personalization scenarios. Importantly, we design a re-usable personalized query expansion component, using semantics of multi-dimensional models and usage statistics from repositories of BI documents like reports or dashboards. This component is an important part of another experimentation we realized, Text-To-Query. This system dynamically generates multi-dimensional queries to illustrate a text and support the knowledge worker in the analysis or enrichment of documents she is manipulating. Besides, we also illustrate the integration and usage of our graph repository and situation management frameworks in an open and extensible federated search project, to provide background knowledge management and personalization.

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 goal of the thesis is first to find available information about user preferences, user feedback and their acquisition, processing, storing etc. The collected information is then used for making suggestions / advices for the creating an recommender system for the web shops (with special emphasis on implicit feedback). The following chapters introduces UPComp - our solution of the recommender system for the web shops. The UPComp is written in the programming language PHP and uses MySQL database. The thesis also includes testing of the UPComp on real-user web shop sites slantour.cz and antikvariat-ichtys.cz.

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.

Recommender Systems (RS) support users to find relevant content, such as movies, books, songs, and other products based on their preferences. Such preferences are gathered by analyzing past users interactions, however, data collected for this purpose are typically prone to sparsity and high dimensionality. Clustering-based techniques have been proposed to handle these problems effectively and efficiently by segmenting the data into a number of similar groups based on predefined characteristics. Although these techniques have gained increasing attention in the recommender systems community, they are usually bound to a particular recommender system and/or require critical parameters, such as the number of clusters. In this work, we present three variants of a general-purpose method to optimally extract users groups from a hierarchical clustering algorithm specifically targeting RS problems. The proposed extraction methods do not require critical parameters and can be applied prior to any recommendation system. Our experiments have shown promising recommendation results in the context of nine well-known public datasets from different domains. / Sistemas de Recomendação auxiliam usuários a encontrar conteúdo relevante, como filmes, livros, músicas entre outros produtos baseando-se em suas preferências. Tais preferências são obtidas ao analisar interações passadas dos usuários, no entanto, dados coletados com esse propósito tendem a tipicamente possuir alta dimensionalidade e esparsidade. Técnicas baseadas em agrupamento de dados têm sido propostas para lidar com esses problemas de foma eficiente e eficaz ao dividir os dados em grupos similares baseando-se em características pré-definidas. Ainda que essas técnicas tenham recebido atenção crescente na comunidade de sistemas de recomendação, tais técnicas são usualmente atreladas a um algoritmo de recomendação específico e/ou requerem parâmetros críticos, como número de grupos. Neste trabalho, apresentamos três variantes de um método de propósitvo geral de extração ótima de grupos em uma hierarquia, atacando especificamente problemas em Sistemas de Recomendação. Os métodos de extração propostos não requerem parâmetros críticos e podem ser aplicados antes de qualquer sistema de recomendação. Os experimentos mostraram resultados promissores no contexto de nove bases de dados públicas conhecidas em diferentes domínios.