National Repository of Grey Literature 40 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Theoretical aspect of modeling of user decision
Lacký, Peter ; Vojtáš, Peter (advisor) ; Vomlelová, Marta (referee)
In this thesis we address to the problematics of modelling user preferences. We discuss different views on user preferences as well as we give an overview of known models of user preferences and compare them. In more detail we introduce Fuzzy Logic Programming, Bayesian Logic Programming, Probabilistic Relational Models and Markov Logic Networks. For each model we propose transformations to other models and we show possible utilizations in real world. Finally we present our suggestions how to extend and improve these models. Powered by TCPDF (www.tcpdf.org)
Constraint Programming in Planning
Surynek, Pavel ; Barták, Roman (advisor) ; Vojtáš, Peter (referee) ; Štěpánková, Olga (referee)
This thesis deals with planning problems and Boolean satisfiability problems that represent major challenges for artificial intelligence. Planning problems are stated as finding a sequence of actions that reaches certain goal. One of the most successful techniques for solving planning problems is a concept of plan- ning graphs and the related GraphPlan algorithm. In the thesis we identified a weak point of the original GraphPlan algorithm which is the search for actions that support certain goal. We proposed to model this problem as a constraint satisfaction problem and we solve it by maintaining arc-consistency. Several propagation variants for maintaining arc-consis- tency in the model are proposed. The model and its solving process were integrated into the general GraphPlan-based planning algorithm. The performed experimental evaluation showed improvements in order of magnitude in several measured characteristics (overall solving time, number of backtracks) compared to the standard version of the GraphPlan algorithm. Next we proposed a stronger consistency technique for pruning the search space during solving the problem of finding supports. It is called projection consistency and it is based on disentangling the structure of the problem formulation. If the problem of finding sup- porting actions is...
Web page data figure finder
Janata, Dominik ; Vojtáš, Peter (advisor) ; Nečaský, Martin (referee)
The thesis treats automatic extraction of semantic data from Web pages. Within this broad problem, it focuses on finding values of data figures within the page presenting certain entity (e.g. price of a laptop). The main idea we wanted to evaluate is that a figure can be found using its context in the page: the words that surround it and values of the attributes of the containing HTML tags, class attribute in particular. Our research revealed there are two types of contemporary solutions of this problem: either the author of the Web page must inline semantic information inside the markup of the page or there are commercial tools that can be trained to parse a particular page format (targetting pages from a single Web domain). We examined the possibilities of developing a general solution that would - for given entity - find its properties across the Web domains using text analysis and machine learning. The naïve algorithm had about 30% accuracy, the lear- ning algorithms had the accuracy between 40 and 50% in finding the properties. Despite the accuracy is not acceptable for a final solution, we believe it confirms the potential of the idea. Keywords: Web pages data extraction 1
Recommender systems - models, methods, experiments
Peška, Ladislav ; Vojtáš, Peter (advisor) ; Jannach, Dietmar (referee) ; Krátký, Michal (referee)
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)
Content based Recommendation from Explicit Ratings
Ferenc, Matej ; Vojtáš, Peter (advisor) ; Peška, Ladislav (referee)
In the thesis we compare several models for prediction of user preferences. The focus is mainly on Content Based models which work with metadata about objects that are recommended. These models are compared with other models which do not use metadata for recommendation. We use three datasets and three metrics to get the results of recommendation. The goal of the thesis is to find out how can the metadata about the users and the objects enhance the standard recommender models. However, the result is that the metadata can enhance recommendation in some cases, but it varies by used metrics and dataset. This enhancement is not significant.
Content-based recommender systems
Michalko, Maria ; Peška, Ladislav (advisor) ; Vojtáš, Peter (referee)
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.
Semantic annotation and querying RDF data
Kýpeť, Jakub ; Vojtáš, Peter (advisor) ; Nečaský, Martin (referee)
Title: Semantic annotation and querying RDF data Author: Jakub Kýpeť Department: Department of Software Engineering Supervisor: Prof. RNDr. Peter Vojtáš, DrSc. Abstract: The presented thesis in detail describes a design and an implementation of self-sustained server application, that allows us to create and manage semantic annotations for various web pages. In the first part it describes the manual annotations and the human interface we have build for them. In the second part it also describes our implementation for a web crawler and an automatic annotation system utilizing this crawler. The last part of the thesis analyzes the testing of this automated system that has been performed using several e- commerce websites with different domains. Keywords: semantic annotation, querying RDF data, user interface, web crawl- ing, automatization
Semantic annotation of domain dependent data
Fišer, Dominik ; Vojtáš, Peter (advisor) ; Kopecký, Michal (referee)
One of the problems of semantic web is automated getting annotated data - web pages. Therefore this work is engaged in manual annotation of web pages and try to simplify this process for users using proposed methods. First part contains analysis of annotated data, users and vocabularies used for annotation. Afterwards are proposed methods which simplify annotation creation for users, the possibility of usage similar annotations or possibility highlight interesting parts of web page suitable for annotation. The work includes proposal of annotation tool user interface also that verifies proposed methods in practice. On the basis of this proposal was created a prototype implementation of the annotation tool Semantic Annotator as an extension for Google Chrome browser, which was also used for experiment verifying user-friendliness especially.
Semantic annotations
Dědek, Jan ; Vojtáš, Peter (advisor) ; Maynard, Diana (referee) ; Železný, Filip (referee)
Four relatively separate topics are presented in the thesis. Each topic represents one particular aspect of the Information Extraction discipline. The first two topics are focused on our information extraction methods based on deep language parsing. The first topic relates to how deep language parsing was used in our extraction method in combination with manually designed extraction rules. The second topic deals with a method for automated induction of extraction rules using Inductive Logic Programming. The third topic of the thesis combines information extraction with rule based reasoning. The core of our extraction method was experimentally reimplemented using semantic web technologies, which allows saving the extraction rules in so called shareable extraction ontologies that are not dependent on the original extraction tool. The last topic of the thesis deals with document classification and fuzzy logic. We are investigating the possibility of using information obtained by information extraction techniques to document classification. Our implementation of so called Fuzzy ILP Classifier was experimentally used for the purpose of document classification.
Acquiring user preferences for eshop
Smrčka, Zdeněk ; Vojtáš, Peter (advisor) ; Eckhardt, Alan (referee)
Title: Acquiring user preferences for e-shop Author: Zdenek Smrcka Department: The Department of Software Engineering Supervisor: prof. RNDr. Peter Vojtáš, DrSc. Abstract: The goal of this thesis is to create e-shop /in medical domain (sector), but useable in other domains (sectors)/. We use implicit metod for acquiring user preferences. By the help of this method the products are ordered from most preferred to less preferred for registered user. In case that user will choose some interesting category of products, then products are listed from maximum to less preferred in that category. It creates feedback about popularity of products for individual users and the producer gets information about interest of products in the market place and administrator can change positions of products in e-shop base on popularity. Keywords: user preferences, internet shop, user's favorite's products

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