Top k recommendations using contextual conditional preferences model

Recommender systems are software tools and techniques which aim at suggesting to users items they might be interested in. Context-aware recommender systems are a particular category of recommender systems which exploit contextual information to provide more adequate recommendations. However, recommendation engines still suffer from the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we introduce a method for generating a list of top k recommendations in a new user cold-start situations. It is based on a user model called Contextual Conditional Preferences and utilizes a satisfiability measure proposed in this paper. We analyze accuracy measures as well as serendipity, novelty and diversity of results obtained using three context-aware publicly available datasets in comparison with several contextual and traditional state-of-the-art baselines. We show that our method is applicable in the new user cold-start situations as well as in typical scenarios

Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.Peer reviewedFinal Published versio

Problemy oceny jakości ontologii

This paper elaborates on what is the quality of the ontology, how to measure the quality of existing ontologies, and how to create high-quality ontologies. A review of existing ontology metrics that can be used to evaluate the quality of ontology is presented. The paper also discusses the problem of quality assurance and quality evaluation of modular ontologies as an important research problem in the face of the prevailing emergence of complicated, difficult to use and modify flat ontologies common in the Semantic Web.W artykule podjęto rozważania na temat tego, czym jest jakość ontologii, jak zmierzyć jakość istniejących ontologii i jak tworzyć ontologie wysokiej jakości. Dokonano przeglądu istniejących metryk ontologii, które mogą posłużyć do oceny jakości ontologii. Przedstawiono problem zapewniania i oceny jakości ontologii modularnych jako ważny problem badawczy w obliczu pojawiania się skomplikowanych, trudnych do użycia i modyfikacji płaskich ontologii

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction

Databases and Information Systems: Contributions from ADBIS 2023 Workshops and Doctoral Consortium

International audienc

Databases and Information Systems: Contributions from ADBIS 2023 Workshops and Doctoral Consortium

International audienceThe 27th European Conference on Advances in Databases and Information Systems (ADBIS) aims at providing a forum where researchers and practitioners in the fields of databases and information systems can interact, exchange ideas and disseminate their accomplishments and visions