Adaptive multi-attribute diversity for recommender systems-Reference-Cited by-同舟云学术

Adaptive multi-attribute diversity for recommender systems

Published:2017-03 Issue: Volume:382-383 Page:234-253
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Di Noia Tommaso^ORCID,Rosati Jessica^ORCID,Tomeo Paolo,Sciascio Eugenio Di

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference49 articles.

1. On unexpectedness in recommender systems: or how to better expect the unexpected;Adamopoulos;ACM Trans. Intell. Syst. Technol.,2014

2. Improving aggregate recommendation diversity using ranking-based techniques;Adomavicius;IEEE Trans. Knowl. Data Eng.,2012

3. A quality based recommender system to disseminate information in a university digital library;Álvaro Tejeda-Lorente;Inf. Sci.,2014

4. Optimal greedy diversity for recommendation.;Ashkan,2015

5. A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition;Barragáns-Martínez;Inf. Sci.,2010

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