Recipe recommendations for individual users and groups in a cooking assistance app
Author:
Funder
Agentschap Innoveren en Ondernemen
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04909-6.pdf
Reference48 articles.
1. Abbas F, Najjar N, Wilson D (2021) Increasing diversity through dynamic critique in conversational recipe recommendations. In: Proceedings of the 13th International Workshop on Multimedia for Cooking and Eating Activities, pp 9–16
2. Abul-Fottouh D, Song MY, Gruzd A (2020) Examining algorithmic biases in youtube’s recommendations of vaccine videos. Int J Medical Inform 140(104):175. https://doi.org/10.1016/j.ijmedinf.2020.104175
3. Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, New York, NY, USA, RecSys ’10, pp 119–126, https://doi.org/10.1145/1864708.1864733
4. Berkovsky S, Freyne J (2010) Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, New York, NY, USA, RecSys ’10, pp 111–118, https://doi.org/10.1145/1864708.1864732, http://doi.acm.org/10.1145/1864708.1864732
5. Bertin-Mahieux T, Ellis DP, Whitman B, et al (2011) The million song dataset. In: Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011)
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