Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Tri Kurniawan"'
Rs4rs is a web application designed to perform semantic search on recent papers from top conferences and journals related to Recommender Systems. Current scholarly search engine tools like Google Scholar, Semantic Scholar, and ResearchGate often yiel
Externí odkaz:
http://arxiv.org/abs/2409.05570
Recommender systems research lacks standardized benchmarks for reproducibility and algorithm comparisons. We introduce RBoard, a novel framework addressing these challenges by providing a comprehensive platform for benchmarking diverse recommendation
Externí odkaz:
http://arxiv.org/abs/2409.05526
The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious f
Externí odkaz:
http://arxiv.org/abs/2309.13080
Autor:
Fabbri, Francesco, Liu, Xianghang, McKenzie, Jack R., Twardowski, Bartlomiej, Wijaya, Tri Kurniawan
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending private data to a central server as in traditional approaches, FL decentrali
Externí odkaz:
http://arxiv.org/abs/2309.08635
In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item i
Externí odkaz:
http://arxiv.org/abs/2308.07222
Publikováno v:
JPFT (Jurnal Pendidikan Fisika dan Teknologi), Vol 1, Iss 2 (2017)
This is quasy experiments research aimed to find out the differences of physics study result through problem based learning model with comic physics assist and conventional learning on students of grade VIII SMPN 1 Labuapi in academic year 2013/2014.
Externí odkaz:
https://doaj.org/article/b5cd7297971343bfa13f24afaa7eba76
Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number of text augmentation techniques have emerged in the field of Natural
Externí odkaz:
http://arxiv.org/abs/2302.12784
Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At
Externí odkaz:
http://arxiv.org/abs/2302.05990
Topic models aim to reveal latent structures within a corpus of text, typically through the use of term-frequency statistics over bag-of-words representations from documents. In recent years, conceptual entities -- interpretable, language-independent
Externí odkaz:
http://arxiv.org/abs/2301.02458
Early detection of relevant locations in a piece of news is especially important in extreme events such as environmental disasters, war conflicts, disease outbreaks, or political turmoils. Additionally, this detection also helps recommender systems t
Externí odkaz:
http://arxiv.org/abs/2212.11856