A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems
Summary
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems is a scholarly article[1].
Key Facts
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-generalized-and-fast-converging-non-negative-latent-factor-model-for-predicting-user-preferences-in-recommender-system
MLA“A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-generalized-and-fast-converging-non-negative-latent-factor-model-for-predicting-user-preferences-in-recommender-system.
BibTeX@misc{4ortxyz_a-generalized-and-fast-converging-non-negative-latent-factor-model-for-predicting-user-preferences-in-recommender-system_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems}}, year = {2026}, url = {https://4ort.xyz/entity/a-generalized-and-fast-converging-non-negative-latent-factor-model-for-predicting-user-preferences-in-recommender-system}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems — https://4ort.xyz/entity/a-generalized-and-fast-converging-non-negative-latent-factor-model-for-predicting-user-preferences-in-recommender-system (retrieved 2026-05-24)
