Optimizing novelty and diversity in recommendations

Author:

Díez JorgeORCID,Martínez-Rego David,Alonso-Betanzos Amparo,Luaces Oscar,Bahamonde Antonio

Funder

Secretaría de Estado de Investigación, Desarrollo e Innovación

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference31 articles.

1. Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)

2. Anderson, C.: The Long Tail: Why the Future of Business is Selling Less of More. Hachette Books, New York (2006)

3. Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 881–918. Springer, Boston (2015)

4. Castells, P., Vargas, S., Wang, J.: Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In: International Workshop on Diversity in Document Retrieval (DDR 2011) at the 33rd European Conference on Information Retrieval (ECIR 2011), pp. 29–36. Citeseer (2011)

5. Chen, C.C., Chen, M.C., Sun, Y.: PVA: a self-adaptive personal view agent. J. Intell. Inf. Syst. 18(2–3), 173–194 (2002)

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