Results 111 to 120 of about 2,553,529 (294)
MARec: Metadata Alignment for cold-start Recommendation
For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can be far larger than the number of clicks. Such sparsity is accentuated in cold-start settings, which makes the efficient use of metadata information of paramount importance ...
Julien Monteil +4 more
openaire +3 more sources
Cold-start visualization recommendation driven by large language models for ocean data analysis
IntroductionMarine data is typically large-scale and complex, requiring effective visualization recommendation systems for data filtering and value extraction.
Xin Li +5 more
doaj +1 more source
Spatiotemporal Multi‐Omic Mapping Reveals Liver‐Muscle Metabolic Crosstalk in Cancer Cachexia
The interactive CCAtlas platform delineates cross‐species, spatiotemporal, and sex‐specific molecular dynamics and metabolic rewiring across organs during cancer cachexia. Hepatic Gamt downregulation curtails hepatic creatine synthesis to trigger systemic creatine insufficiency and consequent skeletal muscle atrophy in LLC tumour‐bearing mice ...
Zihan Tian +12 more
wiley +1 more source
Multi-criteria based Item Recommendation Methods
This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction
Noor Ifada +2 more
doaj +1 more source
Sleep disturbance severity closely tracks hearing loss in a clinical cohort, yet the mechanistic link remains unclear. Acute sleep deprivation is shown to trigger transient cochlear oxidative stress that switches into a self‐sustaining neuroinflammatory state, suppressing BK channels and causing irreversible synaptopathy.
Dan Chen +11 more
wiley +1 more source
Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems
The cold-start issue in recommendation systems refers to the challenge of recommending items or users when minimal or no prior data is available. Meta-learning methods have emerged as a response to this challenge due to their ability to transfer prior ...
Jamallah M. Zawia +6 more
doaj +1 more source
Content-aware Neural Hashing for Cold-start Recommendation
Content-aware recommendation approaches are essential for providing meaningful recommendations for new (i.e.,cold-start) items in a recommender system. We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which ...
Alstrup, Stephen +9 more
core +1 more source
[A&T]MLN is a triple‐modal nanoplatform with siRNA‐loaded LN coated with hybrid ACE2/macrophage membrane. It blocks viral entry, neutralizes IL‐6/IL‐1β/TNF‐α, and delivers siRNA to suppress viral replication. In a murine lung injury model, it attenuates inflammation, offering a multi‐pronged strategy against SARS‐CoV‐2 variants and hyperinflammation ...
Hui Li +19 more
wiley +1 more source
Based on genome size‐functional breadth theory, we introduce and validate a framework that adjudicates conflicting average genome size (AGS) estimates by testing which estimate yields a gap between scaled functional diversity and scaled taxonomic diversity that is consistent with the expected positive association between genome size and gene functional
Huanhuan Zhu +8 more
wiley +1 more source
Prompt Tuning for Item Cold-start Recommendation
The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt learning, a powerful technique used in natural language processing (NLP) to address zero- or few-shot problems, has been adapted for recommender systems to tackle similar ...
Yuezihan Jiang +8 more
openaire +3 more sources

