doc. Ing. Pavel Kordík, Ph.D.

In the recommendation systems, we are currently focusing research on a few open problems that have deep theoretical underpinnings, but whose solutions also have very concrete practical applications. We are exploring the utilization of deep neural networks to reduce the cold start problem of recommender systems, the design of transformers to predict shopping baskets. In the field of general machine learning, we focus on reinforced learning to optimize longer-term metrics such as user satisfaction, on transfer learning methods to incorporate new referral databases, and on using AutoML to optimize the architecture and hyperparameters of recommender systems.

doc. Ing. Pavel Kordík, Ph.D.

Ing. David Pešek

Department of Software Engineering

Startup formation poses difficulties for technology-oriented student founders who are primarily trained in software development but often lack practical skills in refining a product. They are typically taught to select a topic and complete it, rather than engaging in the iterative process of product development that involves pivoting and launching small. Additionally, these founders must transition from being engineers to assuming roles such as HR managers, team leads, and product owners. This thesis investigated how to systemize early-stage startup development for student entrepreneurs. Existing frameworks, such as Lean Startup, Design Sprint, Agile, and Lean Canvas, were analyzed using criteria including their relevance for B2B contexts, their applicability to technological innovations, and the aspects of startup formation they do not address. Interviews with experienced startup founders and industry experts were also conducted to identify additional gaps and areas for improvement. Based on this analysis, the Lean and Execute startup formation framework was developed, specifically tailored to the needs of tech-oriented student founders operating in the European context. The framework emphasized a focused approach centered on understanding user and client needs, combined with iterative improvements, guiding founders to start with a minimal viable product (MVP) aligned with validated user requirements, rather than pursuing unvalidated solutions. The framework also provided structured recommendations on team formation, funding strategies, and product development. Validation was performed through its application to the authors own startup using a qualitative case study approach. The study evaluated effective aspects of the framework, identifying which elements were implemented successfully and why they were beneficial. It also examined limitations of the framework, noting what it failed to address and the resulting consequences, as well as the implications of deviations from the framework. This case study demonstrated the frameworks effectiveness in reducing uncertainty, streamlining decision-making processes, and aligning development efforts with market demands. The findings underscored the value of combining established methodologies, such as the Lean Startup approach and Lean Canvas, with insights from experienced professionals to address the specific challenges faced by student entrepreneurs. This thesis advanced the study of startup formation by offering a practical and tailored solution in the form of a Notion guide. The guide incorporates insights from the Background chapter, synthesizing the analyzed frameworks and interview findings. It provides a visual walkthrough of startup formation up to the launch phase, focusing in each stage on what the founder needs to learn. Based on this knowledge, it outlines specific tasks for execution within their startup.

Thesis on DSpace

doc. Ing. Pavel Kordík, Ph.D.

Ing. Stanislav Kuznetsov, Ph.D.

Department of Software Engineering

This thesis describes the design and implementation of an application for scheduling and sending of personalized content. It gives theoretical background into the topics of content personalization, notifications, and explains how we can improve the problem of alert fatigue by carefully scheduling user notifications. It analyzes use cases and existing solutions, as well as technologies and requirements for this notification scheduling orchestrator. The software is then designed and implemented as an open-source library. It is thoroughly tested and a demo integration is created, acting as a first step in its future real-world usage.

Thesis on DSpace