Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items

Author:

Lee Kibeom,Lee Kyogu

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference28 articles.

1. Getting recommender systems to think outside the box;Abbassi,2009

2. Adomavicius, G., & Kwon, Y. (2011). Maximizing aggregate recommendation diversity: A graph–theoretic approach. In Proceedings of workshop on novelty and diversity in recommender systems (pp. 3–10).

3. Power coefficient as a similarity measure for memory-based collaborative recommender systems;Al-Shamri;Expert Systems with Applications,2014

4. Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities;Anand;Expert Systems with Applications,2011

5. Anderson, C. (2006). The long tail: Why the future of business is selling less of more. Hyperion.

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