Property-Based Collaborative Filtering: A New Paradigm for Semantics-Based, Health-Aware Recommender Systems

BibTex:
Áreas de investigación:	Sin categoría	Año:	2010
Tipo de publicación:	Artículo en conferencia	Palabras clave:	Recommender systems, collaborative filtering, e-health
Autores:	8, 2 7

Título del libro:	5th International Workshop on Semantic Media Adaptation and Personalization (SMAP)

Dirección:	Limassol, Cyprus
Mes:	dic


@conference{smap2010, author = "8,7,2,3", abstract = "Recommender systems aim at solving the problem of information overload by selecting items (commercial products, educational assets, TV programs, etc) that match the users{\&}#039; interests. Recently, there have been approaches to drive the recommendations by the information stored in electronic health records, for which the traditional strategies applied in e-commerce, e-learning, entertainment and other areas have several pitfalls. We address those problems by introducing a filtering strategy centered on the semantic properties that characterize the items and the users. Preliminary experiments are reported that prove the advantages of this strategy, especially in what concerns the treatment given to users with unique preferences and needs.", address = "Limassol, Cyprus", booktitle = "5th International Workshop on Semantic Media Adaptation and Personalization (SMAP)", keywords = "Recommender systems, collaborative filtering, e-health", month = "dic", publisher = "IEEE Computer Society", title = "{P}roperty-{B}ased {C}ollaborative {F}iltering: {A} {N}ew {P}aradigm for {S}emantics-{B}ased, {H}ealth-{A}ware {R}ecommender {S}ystems", year = "2010", }
Abstract:	Recommender systems aim at solving the problem of information overload by selecting items (commercial products, educational assets, TV programs, etc) that match the users' interests. Recently, there have been approaches to drive the recommendations by the information stored in electronic health records, for which the traditional strategies applied in e-commerce, e-learning, entertainment and other areas have several pitfalls. We address those problems by introducing a filtering strategy centered on the semantic properties that characterize the items and the users. Preliminary experiments are reported that prove the advantages of this strategy, especially in what concerns the treatment given to users with unique preferences and needs.

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