Dynamic Clustering Personalization for Recommending Long Tail Items

• Autorzy: Vinícius de Sousa Silva Diogo , Araújo Durão Frederico

Identyfikatory

DOI

10.15439/2020F157

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

Abstrakty

EN

Recommendation strategies are used in several contexts in order to bring potential users closer to products with a strong probability of interest. When recomendations focus on niche items, they are called recommendations in the long tail. In these cases, they also look for less popular items and try to find your target custumer, niche market. This paper proposes a long tail recommendation approach that prioritizes relevance, diversity and popularity of recommended items. For that, a hybrid approach based on two techniques are used. The first is clustering with dynamic parameters that adapt from according to the dataset used and the second is a type of Markov chains for to calculate the distance of interest of a user to an item of relevance for this user. The results show that the techniques used have a better relevance indexes at the same time more diverse and less popular recommendations.

Słowa kluczowe

EN

consumer behaviour; Markov processes; pattern clustering; probability; recommender systems

PL

zachowania konsumentów; procesy Markowa; prawdopodobieństwo; systemy rekomendujące

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 417--425
• Opis fizyczny: Bibliogr. 15 poz., tab., wykr.

Twórcy

autor

Vinícius de Sousa Silva Diogo

• Federal University of Bahia - UFBA Computer Science Departament, Salvador - BA, Brazil
• Federal Institute of Maranhão - IFMA Bacabal - MA, Brazil

autor

Araújo Durão Frederico

• Federal University of Bahia - UFBA Computer Science Departament, Salvador - BA, Brazil

Bibliografia

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Uwagi

1. Track 2: Computer Science & Systems

2. Technical Session: Advances in Computer Science & Systems

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