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Probabilistic Models for Recommender Systems
Ahmadli, Aydin ; Vomlelová, Marta (advisor) ; Peška, Ladislav (referee)
Recommender systems are software tools and techniques providing recommendations to users based on their needs. Today, popular e-commerce sites widely use recommender systems to recommend product items, articles, books, music, etc. In this thesis, we discuss various probabilistic models for recommender systems, and put the most focus on implementation of hybrid and interpretable probabilistic content-based collaborative filtering model, called Collaborative Topic model for Poisson distributed ratings (CTMP) augmented with Bernoulli randomness for Online Maximum a Posteriori Estimation (BOPE). Resulting model outperforms the previously existing models significantly with its main competency being in commercial product recommendations. It is a fast, scalable, and efficient in ill-posed cases, including short text and sparse data. The model is trained and tested on well-known MovieLens 20M and NETFLIX datasets, and empirical evaluations such as recall, precision, sparsity and topic interpretations are promising.
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Web application for searching recipes
Lhoťanová, Kristýna ; Nečaský, Martin (advisor) ; Peška, Ladislav (referee)
This thesis aims to develop a web application for searching recipes. The search for recipes is based on aggregating datasets from the existing recipe websites and extending the data using knowledge graphs. Knowledge graphs were represented by DBpedia and Wikidata projects. These were used to gather data about ingredients. Data were extracted using the Apify web scraping library and stored in the database system Apache CouchDB using the document model. The application provides the user with different options for filtering results, including faceted search. Faceted search is implemented using the Apache Solr platform. The focus is on searching based on ingredients. The web application is a single-page application implemented using the React.js library at the frontend and the Express.js framework at the backend. Both parts of the application are written in statically typed language TypeScript and exchange information through REST API. 1
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Extending self-organizing maps with ranking awareness
Park, Kyung Won ; Peška, Ladislav (advisor) ; Lokoč, Jakub (referee)
Title: Extending Self-organizing Maps with Ranking Awareness Author: Kyung Won Park Department: Department of Software Engineering Supervisor: Mgr. Ladislav Peska, Ph.D., Department of Software Engineering Abstract: The self-organizing map (SOM) is a powerful clustering algorithm which takes high- dimensional data as the input and produces a low-dimensional representation of the data. The SOM provides useful insights into the given data by recognizing similar input vectors and clustering them. However, they take into account only the local similarity of the input data, as opposed to relevance (any external ranking). In this paper, we propose two ranking-aware variants of the SOM in an effort to tackle this issue and incorporate evaluation metrics to evaluate our results. Keywords: self-organizing map, relevence feedback, known-item search
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AI-based Structured Web Data Extraction
Joneš, Jan ; Klímek, Jakub (advisor) ; Peška, Ladislav (referee)
In this thesis, we explore current approaches for automatic web data extraction, define their limitations, and aim to overcome them. We propose a deep learning model to extract structured data from graph and visual representations of web pages. The model is evaluated on an older dataset from 2011 which we augment with missing visual assets, and a new dataset consisting of modern websites. It achieves results competitive with recent work and outperforms our baseline based on a state-of-the-art model by at least 10 percentage points on the F1 score. We ensure the implementation is reproducible and provide a demo of extraction from live pages.
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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)
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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.
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Reservation and inrormation system for travel agency
Peška, Ladislav ; Forst, Libor (advisor) ; Skopal, Tomáš (referee)
The goal of this project is to create Reservation System for small or medium-sized travel agency. Essential is a support for On-line tour's reservation, management of capacities of the tours and also support for search engine optimization. Application is divided into two parts. The internal part allows travel agency employers to manage tours, clients, reservations etc. The part for travel agency clients allows them to search for tours, make reservations and check the state of their reservations. The application is written in PHP programming language and using MySQL database server.
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User preferences in the domain of web shops
Peška, Ladislav ; Vojtáš, Peter (advisor) ; Eckhardt, Alan (referee)
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.
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Presentation layer and interface for a system for information integration and search
Hladík, Tomáš ; Nečaský, Martin (advisor) ; Peška, Ladislav (referee)
The aim of this thesis is to analyse, design and implement a system to present business objects. These objects are accessible through an existing RESTful API of an integration system. The system combines various sources and publishes them together with information how these data are stored. The task of the thesis is to create data views that meet the following requirements - they can react to metadata changes, they support multiple output formats and these views can be customized. The application should be easily modifiable and connectable to already existing web systems.
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Using customer preferences in property market
Strnad, Radek ; Kopecký, Michal (advisor) ; Peška, Ladislav (referee)
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)
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