The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Solving the apparent diversity-accuracy dilemma of recommender systems
Open accessAbstract
Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.
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Citations
Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Cited by
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338
Algorithmic Effects on the Diversity of Consumption on Spotify
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380281 · Cited by 206
“…filtering framework [1], De Choudhury et al. proposed a clustering technique for recommending social media content that matches a specified level of information diversity [7], and Zhou et al. developed a recommendation algorithm that promotes diversity without sacrificing too much accuracy [32].”
BackgroundDesign of efficient computational workflows for in silico drug repurposing
Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., Labat, I., & Zhavoronkov, A. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2016.09.019 · Cited by 150
Diversity and Inclusion Metrics in Subset Selection
Mitchell, M., Baker, D., Moorosi, N., Denton, R., Hutchinson, B., Hanna, A., Gebru, T., & Morgenstern, J. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375832 · Cited by 87
Metadata
- Authors
- Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-02-22
- Volume
- 107
- Issue
- 10
- Pages
- 4511-4515
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2111094216
- Cited by
- 972
- References
- 1
- FWCI
- 143.67
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
