Results 71 to 80 of about 208,309 (312)
Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. Methods Cerebrospinal fluid was obtained from PREDICT‐HD and
Daniel Chelsky +8 more
wiley +1 more source
Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering
Jurnal Computech dan Bisnis, 2018 Laptop is needed for students and for office workers because it is better than a desktop computer. In this era, laptops have a variety of brands and specifications that sometimes make people have difficulty in finding, choosing or buying the right laptop
Anderias Eko Wijaya, Deni Alfian
doaj
A Channel Coding Perspective of Collaborative Filtering
, 2009 We consider the problem of collaborative filtering from a channel coding perspective. We model the underlying rating matrix as a finite alphabet matrix with block constant structure.
Aditya, S. T. +2 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu +14 more
wiley +1 more source
ANALYSIS OF CONTENT RECOMMENDATION METHODS IN INFORMATION SERVICES
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie ŚrodowiskaThe object of the research is the process of selecting a content recommendation method in information services. The study's relevance stems from the rapid development of informational and entertainment resources and the increasing volume of data they ...
Oleksandr Necheporuk +4 more
doaj +1 more source
Novel Neighbor Selection Method to Improve Data Sparsity Problem in Collaborative Filtering
International Journal of Distributed Sensor Networks, 2013 Memory-based collaborative filtering selects the top- k neighbors with high rank similarity in order to predict a rating for an item that the target user has not yet experienced.
Hyeong-Joon Kwon, Kwang Seok Hong
doaj +1 more source
Deriving item features relevance from collaborative domain knowledge [PDF]
, 2018 An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering).
Cremonesi, Paolo +2 more
core +1 more source
Active Collaborative Filtering
, 2012 Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the recommendations made?
Boutilier, Craig +2 more
openaire +2 more sources
Robust collaborative filtering [PDF]
Proceedings of the 2007 ACM conference on Recommender systems, 2007 The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations.
Bhaskar Mehta +2 more
openaire +1 more source
RS on video games based on item-based collaborative filtering algorithm [PDF]
, 2023 Hongyun Zhu
openalex +1 more source