Context Clustering-based Recommender Systems

• Autorzy: Kannout Eyad

Identyfikatory

DOI

10.15439/2020F54

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet. Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent. The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems. The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations.

Słowa kluczowe

EN

collaborative filtering; context-aware recommender system; contextual information; clustering; accuracy of prediction; quality of recommendations

PL

filtrowanie zespołowe; kontekstowy system rekomendacji; Informacja kontekstowa; grupowanie; trafność prognozy; jakość rekomendacji

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 85--91
• Opis fizyczny: Bibliogr. 17 poz., wykr., tab., wz.

Twórcy

autor

Kannout Eyad

• Institute of Informatics, University of Warsaw, Banacha 2, Warsaw, Poland

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).