Hybrid Recommender Systems: A Systematic Literature Review

Tipo di pubblicazione: Articolo su rivista
Tipologia MIUR: Contributo su Rivista > Articolo in rivista
Titolo: Hybrid Recommender Systems: A Systematic Literature Review
Autori: Çano, Erion; Morisio, Maurizio
Autori di ateneo:
Titolo del periodico: INTELLIGENT DATA ANALYSIS
Tipo di referee: Esperti anonimi
Editore: IOS Publishing House
Volume: 21
Numero: 6
Numero di pagine: 38
ISSN: 1088-467X
Abstract: Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.
Status: In stampa
Lingua della pubblicazione: Inglese
Parole chiave: recommender systems, systematic review, recommendation strategies, hybrid recommendations, hybrid recommendations, recommender systems, systematic review, recommendation strategies
Dipartimenti (originale): DAUIN - Dipartimento di Automatica Informatica
Dipartimenti: DAUIN - Dipartimento di Automatica e Informatica
URL correlate:
    Area disciplinare: Area 09 - Ingegneria industriale e dell'informazione > SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
    Data di deposito: 12 Dic 2016 15:56
    Data ultima modifica (IRIS): 08 Feb 2017 12:58:05
    Data inserimento (PORTO): 10 Feb 2017 11:20
    Permalink: http://porto.polito.it/id/eprint/2659069
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