Search
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.
Citations
-
0
CrossRef
-
0
Web of Science
-
3
Scopus
Authors (3)
-
Tommaso Di Noia
- Politecnico di Bari
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.15439/2017F258
- License
-
open in new tab
Keywords
Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Published in:
-
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
seen 101 times
Recommended for you
Rating Prediction with Contextual Conditional Preferences
- A. Karpus,
- T. D. Noia,
- P. Tomeo
- + 1 authors