Background: Diet-related Health Recommender Systems (HRS) have gained attention for their potential to provide personalized dietary guidance, particularly for patients with chronic conditions. However ...

Google published a research paper about helping recommender systems understand what users mean when they interact with them. Their goal with this new approach is to overcome the limitations inherent ...

Abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the ...

There once was a time where going viral on the internet actually meant something. Long ago, in the early 2010s, 500,000 views could actually land you on daytime TV, where you could experience the ...

ABSTRACT: The offline course “Home Plant Health Care,” which is available to the senior population, serves as the study object for this paper. Learn how to use artificial intelligence technologies to ...

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...

recsys-lite is a small, teach-first recommender toolkit that runs in constrained environments and scales to real data. Use it from the CLI or Python to load interaction data, train classic ...

As an instructor and leader, the fire officer plays a critical role in shaping the skills, confidence, and synergy of your crew. Training isn’t just about achieving compliance with departmental ...

Yandex has recently made a significant contribution to the recommender systems community by releasing Yambda, the world’s largest publicly available dataset for recommender system research and ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...