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Contextual modeling content-based approaches for new-item recommendation

Author
Codina, V.; Oliva, L.
Type of activity
Presentation of work at congresses
Name of edition
5th Information Interaction in Context Symposium
Date of publication
2014
Presentation's date
2014-08
Book of congress proceedings
Proceedings of the 5th Information Interaction in Context Symposium, IIiX 2014
First page
259
Last page
262
Publisher
ACM
DOI
https://doi.org/10.1145/2637002.2637037 Open in new window
Repository
http://hdl.handle.net/2117/24621 Open in new window
URL
http://dl.acm.org/citation.cfm?id=2637002.2637037&coll=DL&dl=GUIDE&CFID=453517717&CFTOKEN=15049451 Open in new window
Abstract
The new-item cold-start problem is a well-known limitation of context-free and context-aware Collaborative Filtering (CF) prediction models. In such situations, only Content-based (CB) approaches can produce meaningful recommendations. In this paper, we propose three Context-Aware Content-Based (CACB) models that extend a linear CB prediction model with context-awareness by including additional parameters that represent the influence of context with respect to the users' interests and rating beh...
Citation
Codina, V.; Oliva, L. Contextual modeling content-based approaches for new-item recommendation. A: Information Interaction in Context Symposium. "Proceedings of the 5th Information Interaction in Context Symposium, IIiX 2014". Regensburg: ACM, 2014, p. 259-262.
Keywords
cold-start problem, content-based filtering, context-aware recommender systems, contextual modelling
Group of research
KEMLG - Knowledge Engineering and Machine Learning Group

Participants