Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
CEUR-WSDate
2015Citation
IntRS 2015 Interfaces and Human Decision Making for Recommender Systems: Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2015). Ed. John O'Donovan, Alexander Felfernig, Nava Tintarev, Peter Brusilovsky, Giovanni Semeraro, Pasquale Lops. CEUR Workshop Proceedings. Volume 1438, 2015. 2-8ISSN
1613-0073Editor's Version
http://ceur-ws.org/Vol-1438/Subjects
Feature Selection; Decision making; Feature extraction; Context dependent; Context information; Context-aware recommender systems; Contextual factors; Contextual information; Experimental evaluation; Selection methods; System's performance; Recommender systems; InformáticaNote
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Rights
© 2015 The author/sAbstract
Context-Aware Recommender System (CARS) models are
trained on datasets of context-dependent user preferences
(ratings and context information). Since the number of
context-dependent preferences increases exponentially with
the number of contextual factors, and certain contextual in-
formation is still hard to acquire automatically (e.g., the
user's mood or for whom the user is buying the searched
item) it is fundamental to identify and acquire those factors
that truly in
uence the user preferences and the ratings. In
particular, this ensures that (i) the user e ort in specifying
contextual information is kept to a minimum, and (ii) the
system's performance is not negatively impacted by irrele-
vant contextual information. In this paper, we propose a
novel method which, unlike existing ones, directly estimates
the impact of context on rating predictions and adaptively
identi es the contextual factors that are deemed to be useful
to be elicited from the users. Our experimental evaluation
shows that it compares favourably to various state-of-the-art
context selection methods.
Files in this item
Google Scholar:Braunhofer, Matthias
-
Fernández-Tobías, Ignacio
-
Ricci, Francesco
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