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Top k Recommendations using Contextual Conditional Preferences Model

Abstrakt

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.

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Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
materiały konferencyjne indeksowane w Web of Science
Opublikowano w:
Annals of Computer Science and Information Systems nr 11, strony 19 - 28,
ISSN: 2300-5963
Tytuł wydania:
Proceedings of the 2017 Federated Conference on Computer Science and Information Systems strony 19 - 28
ISSN:
2300-5963
Język:
angielski
Rok wydania:
2017
Opis bibliograficzny:
Karpus A., Noia T. D., Goczyła K..: Top k Recommendations using Contextual Conditional Preferences Model, W: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, 2017, ,.
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.15439/2017f258
Weryfikacja:
Politechnika Gdańska

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