In this thesis we discuss the issue of searching the best k objects from the multi-users point of view. Every user has his own preferences, which are represented by fuzzy functions and aggregation function. This thesis designs and implements several solutions of searching the best k objects when attributes data are stored on remote servers. It was necessary to modificate existing algorithms for this type of obtaining data. This thesis uses several variants of Fagin algorithm, indexing methods using B+ trees and communication via web services.

In recent years the market share of major real estate companies, at least the Czech ones, has not changed much. Statistical data don't reflect any significal upward trend in volumes of properties for rent or sale. In case the real estate company would like to access larger market share, they have to secure a competitive advantage over the others. One of the ways how to attract more potential customers might be speeding up the company website's property search process. In many cases the website visitors are facing hundreds or thousands of property offers before finding couple satisfactories. The aim of the thesis is to explore possibilities of applicating customer preferences in property trading. The focus is put on research of recommender system algorithms, their characteristics and limtations. The author is evaluating usage of each algorithm variant and its suitability for a real world deployment in a real estate area. Apart from the theoretical part of the work one can find a part, where real estate information system is extended with a framework for implementing recommendation system algorithms. The author is in possesion of production data of a medium sized real estate company. He uses the recommender system framework to build and evaluate example algorithm. Powered by TCPDF (www.tcpdf.org)

This thesis investigates the area of preference learning and recommender systems. We concentrated recommending on small e-commerce vendors and efficient usage of implicit feedback. In contrast to the most published studies, we focused on investigating multiple diverse implicit indicators of user preference and substantial part of the thesis aims on defining implicit feedback, models of its combination and aggregation and also algorithms employing them in preference learning and recommending tasks. Furthermore, a part of the thesis focuses on other challenges of deploying recommender systems on small e-commerce vendors such as which recommending algorithms should be used or how to employ third party data in order to improve recommendations. The proposed models, methods and algorithms were evaluated in both off-line and on-line experiments on real world datasets and on real e-commerce vendors respectively. Datasets are included to the thesis for the sake of validation and further research. Powered by TCPDF (www.tcpdf.org)

Immersive technologies, including augmented reality (AR) and virtual reality (VR), are envisioned to become ubiquitous in future work environments. The implementation of both technologies is associated with versatile benefits, such as decreased costs, reduced physical risks, increased employee self-satisfaction, and lower resource consumption. Despite these potential benefits, the organizational diffusion of immersive technologies faces myriad challenges. For instance, usability problems along with privacy concerns have introduced technology acceptance issues. Addressing these challenges, this cumulative dissertation explores the design, application, and implications of AR and VR systems in the workplace by employing a mixed-methods approach. The contribution of this research is threefold. First, this dissertation provides descriptive insights into user preferences for immersive technologies to inform user-centered design considerations. Second, this dissertation presents design principles to guide the development of four information technology artifacts. Two of these artifacts enable VR-based collaboration in the fields of design thinking and process modeling, while the remaining two artifacts leverage AR to facilitate the crowdsourcing of human intelligence tasks and to support students in distance learning settings. Third, this dissertation develops an e³-value model for the AR and VR business ecosystem to illustrate how technology providers can transform such artifacts into economic value. Taken together, these insights improve understanding the sociotechnical interplay between humans, tasks, and immersive technologies, as well as its economic implications.

This work deals with the issue of poviding recommendations for individual users of e-shop based on the obtained user preferences. The work includes an overview of existing recommender systems, their methods of getting user preferences, the methods of using objects' content and recommender algorithms. An integral part of this work is design and implementated for independent software component for Content-based recommendation. Component is able to receive various user preferences and various forms of object's input data. The component also contains various processing methods for implicit feedback and various methods for making recommendations. Component is written in the Java programming language and uses a PostgreSQL database. The thesis also includes experiments that was carried out with usage of component designed on datasets slantour.cz and antikvariat-ichtys.cz e-shops.

Title: Preference Top-k Search Based on Multidimensional B-Tree Author: RNDr. Matúš Ondreička Department: Department of Software Engineering Faculty of Mathematics and Physics Charles University in Prague Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc. Author's e-mail address: ondreicka@ksi.mff.cuni.cz Supervisor's e-mail address: pokorny@ksi.mff.cuni.cz Abstract: In this thesis, we focus on the top-k search according to user pref- erences by using B+ -trees and the multidimensional B-tree (MDB-tree). We use model of user preferences based on fuzzy functions, which enable us to search according to a non-monotone ranking function. We propose model of sorted list based on the B+ -tree, which enables Fagin's algorithms to search for the top-k objects according to a non-monotone ranking function. We apply this model in the Internet environment with data on different remote servers. Furthermore, we designed novel dynamic tree-based data structures, namely, MDB-tree composed of B+ -trees, MDB-tree with lists, MDB-tree with groups of B+ -trees and multiple-ordered MDB-tree. Concurrently, we have developed novel top-k algorithms, namely, the MD algorithm, the MXT algorithm and their variants which are able search for the top-k objects ac- cording to a non-monotone ranking function. These top-k algorithms are efficient...

Sistemas de recomendação fazem parte do nosso dia-a-dia. Os métodos usados nesses sistemas tem como objetivo principal predizer as preferências por novos itens baseado no perĄl do usuário. As pesquisas relacionadas a esse tópico procuram entre outras coisas tratar o problema do cold-start do usuário, que é o desaĄo de recomendar itens para usuários que possuem poucos ou nenhum registro de preferências no sistema. Uma forma de tratar o cold-start do usuário é buscar inferir as preferências dos usuários a partir de informações adicionais. Dessa forma, informações adicionais de diferentes tipos podem ser exploradas nas pesquisas. Alguns estudos usam informação social combinada com preferências dos usuários, outros se baseiam nos clicks ao navegar por sites Web, informação de localização geográĄca, percepção visual, informação de contexto, etc. A abordagem típica desses sistemas é usar informação adicional para construir um modelo de predição para cada usuário. Além desse processo ser mais complexo, para usuários full cold-start (sem preferências identiĄcadas pelo sistema) em particular, a maioria dos sistemas de recomendação apresentam um baixo desempenho. O trabalho aqui apresentado, por outro lado, propõe que novos usuários receberão recomendações mais acuradas de modelos de predição que já existem no sistema. Nesta tese foram propostas 4 abordagens para lidar com o problema de cold-start do usuário usando modelos existentes nos sistemas de recomendação. As abordagens apresentadas trataram os seguintes aspectos: o Inclusão de informação social em sistemas de recomendação tradicional: foram investigados os papéis de várias métricas sociais em um sistema de recomendação de preferências pairwise fornecendo subsidíos para a deĄnição de um framework geral para incluir informação social em abordagens tradicionais. o Uso de similaridade por percepção visual: usando a similaridade por percepção visual foram inferidas redes, conectando usuários similares, para serem usadas na seleção de modelos de predição para novos usuários. o Análise dos benefícios de um framework geral para incluir informação de redes de usuários em sistemas de recomendação: representando diferentes tipos de informação adicional como uma rede de usuários, foi investigado como as redes de usuários podem ser incluídas nos sistemas de recomendação de maneira a beneĄciar a recomendação para usuários cold-start. o Análise do impacto da seleção de modelos de predição para usuários cold-start: a última abordagem proposta considerou que sem a informação adicional o sistema poderia recomendar para novos usuários fazendo a troca entre os modelos já existentes no sistema e procurando aprender qual seria o mais adequado para a recomendação. As abordagens propostas foram avaliadas em termos da qualidade da predição e da qualidade do ranking em banco de dados reais e de diferentes domínios. Os resultados obtidos demonstraram que as abordagens propostas atingiram melhores resultados que os métodos do estado da arte. / Recommender systems are in our everyday life. The recommendation methods have as main purpose to predict preferences for new items based on userŠs past preferences. The research related to this topic seeks among other things to discuss user cold-start problem, which is the challenge of recommending to users with few or no preferences records. One way to address cold-start issues is to infer the missing data relying on side information. Side information of different types has been explored in researches. Some studies use social information combined with usersŠ preferences, others user click behavior, location-based information, userŠs visual perception, contextual information, etc. The typical approach is to use side information to build one prediction model for each cold user. Due to the inherent complexity of this prediction process, for full cold-start user in particular, the performance of most recommender systems falls a great deal. We, rather, propose that cold users are best served by models already built in system. In this thesis we propose 4 approaches to deal with user cold-start problem using existing models available for analysis in the recommender systems. We cover the follow aspects: o Embedding social information into traditional recommender systems: We investigate the role of several social metrics on pairwise preference recommendations and provide the Ąrst steps towards a general framework to incorporate social information in traditional approaches. o Improving recommendation with visual perception similarities: We extract networks connecting users with similar visual perception and use them to come up with prediction models that maximize the information gained from cold users. o Analyzing the beneĄts of general framework to incorporate networked information into recommender systems: Representing different types of side information as a user network, we investigated how to incorporate networked information into recommender systems to understand the beneĄts of it in the context of cold user recommendation. o Analyzing the impact of prediction model selection for cold users: The last proposal consider that without side information the system will recommend to cold users based on the switch of models already built in system. We evaluated the proposed approaches in terms of prediction quality and ranking quality in real-world datasets under different recommendation domains. The experiments showed that our approaches achieve better results than the comparison methods. / Tese (Doutorado)

No description available.

Autonomes Fahren (AF) wird potenziell die Präferenzen für die im Auto verbrachte Zeit stark beeinflussen und dementsprechend den Wert der Reisezeit, der ein Schlüsselelement von Kosten-Nutzen-Analysen im Verkehr ist. Die Untersuchung dieses Aspekts des AF ist daher entscheidend für die Analyse potenzieller Auswirkungen der Technik auf die zukünftige Verkehrsnachfrage. Trotz der steigenden Anzahl an Studien zu diesem Thema, gibt es noch erhebliche Forschungslücken. Der Fokus der Dissertation ist die potenziellen Änderungen des Reisezeitwerts, die durch das AF entstehen, zu messen sowie ihre Determinanten zu analysieren. Es wurden sowohl qualitative Ansätze als auch quantitative Methoden verwendet. Dabei wurden zwei Konzepte von AF betrachtet: privates und geteiltes autnomes Fahrzeug. Die Ergebnisse der Analysen zeigen einen niedrigeren Wert der Reisezeitersparnis beim AF im Vergleich zum manuellen Fahren, allerdings nur auf Pendelwegen. Das private Fahrzeug wird als eine attraktivere Option als ein geteiltes Fahrzeug wahrgenommen, jedoch unterscheiden sich die Nutzerpräferenzen für geteilte Fahrzeug stark zwischen den durchgeführten Studien. Individuelle Charakteristiken, wie Erfahrung mit Fahrassistenzsystemen, beeinflussen stark die Wahrnehmung der Zeit im AF; andere sozio-demographischen Faktoren, wie Alter und Geschlecht haben vor allem einen indirekten Effekt auf den Reisezeitwert indem sie Einstellungen potenzieller Nutzer beeinflussen. Die Verbesserung des Fahrterlebnisses durch das AF und das Vertrauen in die Technik sind wichtige Determinanten der Reisezeitwahrnehmung. Fahrvergnügen und andere wahrgenommene Vorteile vom manuellen Fahren gleichen in einem gewissen Ausmaß den Nutzen vom AF aus. Es wurden Reisezeitwerte für unterschiedliche potenzielle Nutzersegmente berechnet. Abschließend wurden politische Implikationen, Empfehlungen für die Entwicklung von AF sowie Empfehlungen für künftige Studien und potenziellen Forschungsgebiete abgeleitet. / Autonomous driving will potentially strongly affect preferences for time spent in a vehicle and, consequently, the value of travel time savings (VTTS). As VTTS is a key element of cost-benefit analysis for transport, these interrelations are crucial for analysing the potential impact of the technology on future travel demand. Despite the increasing number of studies dedicated to this topic there are still many unanswered questions. The focus of the thesis is to measure potential changes in the VTTS resulting from the introduction of autonomous driving and analyse their determinants. Qualitative approaches and quantitative methods were used. Two concepts of AVs were considered: a privately-owned AV (PAV) and a shared AV (SAV). The analysis results suggest lower VTTS for autonomous driving compared to manual driving, but only on commuting trips. A PAV is perceived as a more attractive option than an SAV, but user preferences for SAVs vary between the conducted studies. Individual characteristics, such as experience with advanced driver assistance systems, strongly affect the perception of time in an AV; other socio-demographic factors, such as age and gender, affect mode choices and the VTTS mainly indirectly by influencing the attitudes of potential users. The improvement in travel experiences due to autonomous driving and trust in the technology are important determinants of the perception of travel time. Enjoyment of driving and other perceived benefits of manual driving partially counterbalance the utility of riding autonomously. VTTS for different potential user segments were calculated. In conclusion, several policy implications, development recommendations for AVs as well as recommendations for future studies and potential research avenues are derived from the findings.