Results 101 to 110 of about 23,764 (310)

Learning users' interests by quality classification in market-based recommender systems

, 2005
Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations.
Wei, Y.Z., L. Moreau, Wei, YZ, Jennings, N. R., Nicholas R. Jennings, Moreau, L., Y.Z. Wei, Luc Moreau, N.R. Jennings, Moreau, L, Jennings, NR, Yan Zheng Wei   +11 more
core   +1 more source

Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer

FEBS Open Bio, EarlyView.
A novel signature integrating genome‐wide analysis with clinical factors predicts recurrence in stage II colorectal cancer and enables a new risk stratification to guide postoperative adjuvant chemotherapy. Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential ...
Mayuko Otomo, Keisuke Okuno, Shuichi Watanabe, Sakiko Oba, Hiroyasu Kagawa, Masanori Tokunaga, Daisuke Ban, Yusuke Kinugasa   +7 more
wiley   +1 more source

Artist Considerations in Offline Evaluation of Music Recommender Systems [PDF]

Many modern research works in the field of music recommender systems (MRSs) evaluate performance by song ranking accuracy in offline data splits. Although this paradigm matches widely adopted methodology in the broader recommender systems research ...
Meehan, G, rd Music Recommender Systems Workshop, Pauwels, J   +2 more
core   +2 more sources

Progettazione di un Service Recommender System basato su Intelligenza Collettiva [PDF]

, 2011
L’obiettivo della tesi è stato quello di determinare un approccio innovativo ai Recommender System, con particolare enfasi su quelli per i servizi. La proposta avanzata consiste nell’applicazione dell’intelligenza collettiva a questi sistemi.
Zanchetta, Marco
core  

YIPFα1A expression is regulated by multilayered molecular mechanisms

FEBS Open Bio, EarlyView.
YIPFα1A, a five‐pass Golgi protein, is regulated at multiple layers. (1) Rare‐codon enrichment drives translation‐coupled mRNA decay. (2) A proximal 3′‐UTR element stabilizes mRNA. (3) A distal 3′‐UTR element included by alternate poly(A) site usage represses translation, which can be overridden by the proximal 3′‐UTR element.
Tokio Takaji, Yurika Nakanishi, Nobuhiro Nakamura   +2 more
wiley   +1 more source

Survey on the applications of large language models in recommender systems

大数据
The rise of large language model (LLM) has brought new opportunities to recommender systems. However, existing research mainly focuses on the technical frameworks and engineering implementations of LLM-based recommender systems, lacking a systematic ...
Xu Xiaoying, Liao Wenjie, Wang Hanlin
doaj  

Recommender Systems: A Market Based Design

, 2003
Recommender systems have been widely advocated as a way of coping with the problem of information overload for knowledge workers. Given this, multiple recommendation methods have been developed.
Wei, Yan Zheng, Jennings, N. R., Jennings, NR, Moreau, Luc   +3 more
core  

Derivation and characterization of retinal pigment epithelium from urine‐derived iPSCs

FEBS Open Bio, EarlyView.
Age‐related macular degeneration causes vision loss via RPE dysfunction and loss. Traditional iPSC therapies rely on invasive biopsies, limiting scalability. Here, we utilize urine‐derived stem cells as an accessible source to generate u‐iPSCs, successfully differentiated into pigmented RPE. This “Urine‐to‐Retina” platform provides a promising path for
Daniella Beiner, Hainan Zhu, Carol Christine Bosholm, Heuy‐Ching Wang, Tracy Criswell, Anthony Atala, Jian‐Xing Ma, Yuanyuan Zhang   +7 more
wiley   +1 more source

Emerging trends of recommender system for e-commerce: a comprehensive review

Discover Computing
With the rapid growth of online information, the recommender system (RSs) has become essential tools for filtering information and generating personalized recommendations for customers. These systems analyze individual user preferences, past reviews, and
Chour Singh Rajpoot, Varun Tiwari, Santosh Kumar Vishwakarma   +2 more
doaj   +1 more source

Evaluating Group Recommender Systems

, 2018
In the previous chapters, we have learned how to design group recommender systems but did not explicitly discuss how to evaluate them. The evaluation techniques for group recommender systems are often the same or similar to those that are used for single
Trattner, Christoph,, Said, Alan,, Boratto, Ludovico,, Felfernig, Alexander,   +3 more
core