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All Outputs (36)

Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing (2019)
Presentation / Conference Contribution
Tashu, T. M., Szabó, D., & Horváth, T. (2019, June). Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing. Presented at ITS2019 Conference, Kingston, Jamaica

Automated essay evaluation systems use machine learning models to predict the score for an essay. For such, a training essay set is required which is usually created by human requiring time-consuming effort. Popular choice for scoring is a nearest ne... Read More about Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing.

Intelligent On-line Exam Management and Evaluation System (2019)
Presentation / Conference Contribution
Tashu, T. M., Esclamado, J. P., & Horvath, T. (2019, June). Intelligent On-line Exam Management and Evaluation System. Presented at 15th International Conference, ITS 2019, Kingston, Jamaica

Educational assessment plays a central role in the teaching-learning process as a tool for evaluating students’ knowledge of the concepts associated with the learning objectives. The evaluation and scoring of essay answers is a process, besides being... Read More about Intelligent On-line Exam Management and Evaluation System.

Swarm intelligence techniques in recommender systems - A review of recent research (2019)
Journal Article
Peška, L., Tashu, T. M., & Horváth, T. (2019). Swarm intelligence techniques in recommender systems - A review of recent research. Swarm and Evolutionary Computation, 48, 201-219. https://doi.org/10.1016/j.swevo.2019.04.003

One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS.... Read More about Swarm intelligence techniques in recommender systems - A review of recent research.

New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings (2018)
Presentation / Conference Contribution
(2018, September). New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings. Presented at 22th European Conference on Advances in Databases and Information Systems, ADBIS 2018, Budapest, Hungary

Evolutionary computing in recommender systems: a review of recent research (2016)
Journal Article
Horváth, T., & de Carvalho, A. C. P. L. F. (2017). Evolutionary computing in recommender systems: a review of recent research. Natural Computing, 16(3), 441-462. https://doi.org/10.1007/s11047-016-9540-y

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. O... Read More about Evolutionary computing in recommender systems: a review of recent research.

Ranking Formal Concepts by Utilizing Matrix Factorization (2014)
Presentation / Conference Contribution
Pisková, L., Horvath, T., & Krajči, S. Ranking Formal Concepts by Utilizing Matrix Factorization. Presented at 12th International Conference on Formal Concept Analysis, Cluj-Napoca, Romania

Formal Concept Analysis often produce huge number of formal concepts even for small input data. Such a large amount of formal concepts, which is intractable to analyze for humans, calls for a kind of
a ranking of formal concepts according to their i... Read More about Ranking Formal Concepts by Utilizing Matrix Factorization.

Buried pipe localization using an iterative geometric clustering on GPR data (2013)
Journal Article
Janning, R., Busche, A., Horváth, T., & Schmidt-Thieme, L. (2014). Buried pipe localization using an iterative geometric clustering on GPR data. Artificial Intelligence Review, 42(3), 403-425. https://doi.org/10.1007/s10462-013-9410-2

Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects cause hyperbola shaped reflections in the radargram images achieved by GPR. Ori... Read More about Buried pipe localization using an iterative geometric clustering on GPR data.

Factorization Techniques for Predicting Student Performance (2012)
Book Chapter
Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., Nanopoulos, A., & Schmidt-Thieme, L. (2012). Factorization Techniques for Predicting Student Performance. In O. C. Santos, & J. G. Boticario (Eds.), Educational Recommender Systems and Technologies: Practices and Challenges (129-153). IGI Global. https://doi.org/10.4018/978-1-61350-489-5.ch006

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to students. This chapter introduces state-of-the-art recommender system techni... Read More about Factorization Techniques for Predicting Student Performance.

GRAMOFON: General model-selection framework based on networks (2011)
Journal Article
Buza, K., Nanopoulos, A., Horváth, T., & Schmidt-Thieme, L. (2012). GRAMOFON: General model-selection framework based on networks. Neurocomputing, 75(1), 163-170. https://doi.org/10.1016/j.neucom.2011.02.026

Ensembles constitute one of the most prominent class of hybrid prediction models. One basically assumes that different models compensate each other's errors if one combines them in an appropriate way. Often, a large number of various prediction model... Read More about GRAMOFON: General model-selection framework based on networks.

A Model of User Preference Learning for Content-Based Recommender Systems (2009)
Journal Article
Horvath, T. (2009). A Model of User Preference Learning for Content-Based Recommender Systems. Computing and Informatics, 28(4), 1001-1029

This paper focuses to a formal model of user preference learning for
content-based recommender systems. First, some fundamental and special requirements to user preference learning are identified and proposed. Three learning tasks are introduced as... Read More about A Model of User Preference Learning for Content-Based Recommender Systems.

User Preference Web Search -- Experiments with a System Connecting Web and User (2009)
Journal Article
Gurský, P., Horvath, T., Jirásek, J., Krajči, S., Novotny, R., Pribolová, J., Vaneková, V., & Vojtáš, P. (2009). User Preference Web Search -- Experiments with a System Connecting Web and User. Computing and Informatics, 28(4), 1001-1033

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware support... Read More about User Preference Web Search -- Experiments with a System Connecting Web and User.

Knowledge Processing for Web Search – An Integrated Model and Experiments (2008)
Presentation / Conference Contribution
Gurský, P., Horvath, T., Jirásek, J., Novotny, R., Pribolová, J., Vaneková, V., & Vojtáš, P. Knowledge Processing for Web Search – An Integrated Model and Experiments. Presented at Symposium on Intelligent and Distributed Computing IDC, Craiova, Romania

We propose a model of a middleware system enabling personalized web
search for users with different preferences. We integrate both inductive and deductive tasks to find user preferences and consequently best objects. The model is based on modeling p... Read More about Knowledge Processing for Web Search – An Integrated Model and Experiments.

Integration of two fuzzy data mining methods (2004)
Journal Article
Horvath, T., & Krajči, S. (2004). Integration of two fuzzy data mining methods. Neural Network World, 14(5), 391-402

The cluster analysis and the formal concept analysis are both used to identify significiant groups of similar objects. Rice & Siff's algorithm for the clustering joins these two methods in the case where the values of an object-attribute model are 1... Read More about Integration of two fuzzy data mining methods.

An ILP model for a monotone graded classification problem (2004)
Presentation / Conference Contribution
Vojtáš, P., Horvath, T., Krajči, S., & Lencses, R. An ILP model for a monotone graded classification problem. Presented at Znalosti 2003, Ostrava, Czech Republic

Motivation for this paper are classification problems in which data can not be clearly divided into positive and negative examples, especially data in which there is a monotone hierarchy (degree, preference) of more or less positive (negative) exampl... Read More about An ILP model for a monotone graded classification problem.