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Abstract
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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Authors (3)
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Tommaso Di Noia
- Politecnico di Bari
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.15439/2017F258
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- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Published in:
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Annals of Computer Science and Information Systems
no. 11,
pages 19 - 28,
ISSN: 2300-5963 - Title of issue:
- Proceedings of the 2017 Federated Conference on Computer Science and Information Systems strony 19 - 28
- ISSN:
- 2300-5963
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- 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:
- Digital Object Identifier (open in new tab) 10.15439/2017f258
- Verified by:
- Gdańsk University of Technology
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