97,826 Hits in 5.6 sec

International Coordination of Multi-Messenger Transient Observations in the 2020s and Beyond: Kavli-IAU White Paper [article]

S. Bradley Cenko, Patricia A. Whitelock, Laura Cadonati, Valerie Connaughton, Roger Davies, Rob Fender, Paul J. Groot, Mansi M. Kasliwal, Tara Murphy, Samaya Nissanke, Alberto Sesana, Shigeru Yoshida (+1 others)
2020 arXiv   pre-print
astronomy, identifying eight broad areas of concern.  ...  These recommendations are not only aimed at collaborative groups and individuals, but also at the various organizations who are essential to making transient collaborations efficient and effective: including  ...  We strongly recommend the IAU work with relevant journals to establish uniform standards for data reporting on transient and multi-messenger sources (as well as static sources, for that matter).  ... 
arXiv:2007.05546v1 fatcat:lvs4dmdzkndwnlma6nfagmzxlq

Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

Venkatesan Thillainayagam, Saravanan Kunjithapatham, Ramkumar Thirunavukarasu
2018 International Journal of Engineering & Technology  
To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature  ...  One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items.  ...  The model utilizes the strategy of multi-view learning to perform recommendations.  ... 
doi:10.14419/ijet.v7i4.16002 fatcat:dpltic57jjchjbdv5jywb6khs4


Xushbaqov Sherzod, Khamraev Mansur, Bakhtiyorova Mokhiruy
2022 Zenodo  
Then, the details of deep learning-based collaborative filtering recommender systems are provided.  ...  In this article, we describe deep learning-based recommender systems. First, we introduce deep learning in content-based recommender systems.  ...  Explainability of deep learning based recommender system Deep learning-based recommender systems use the deep learning model to directly predict the user preferences by using multi-source heterogeneous  ... 
doi:10.5281/zenodo.6473945 fatcat:kwzv2uc25ngjzg5sxver7yp2ke

Editorial: A Global Platform for Innovations in Health Professions Education

Javaid I Sheikh, MD, MBA, Victor J. Dzau, MD
2015 Innovations in Global Health Professions Education  
Notable major recommendations in the Commission's reportvalidated the needs for a global outlook, alignment with healthcare delivery, aninclusive multi-professional/trans-professional orientation, a systems-basedapproach  ...  A broad receptivityworldwide for such a paradigm-changing approach to health professionseducation has been facilitated by general dissatisfaction with current healthcareand education globally.  ...  HPE/IPE model, preparing health professionals for a team-based collaborative approach to healthcare.  ... 
doi:10.20421/ighpe2015.1 fatcat:nps3v3dqjfhyvjbaovx2jjeeb4

An Architecture for Developing Educational Recommender Systems

Maritza Bustos-López, Raquel Vásquez-Ramírez, Giner Alor-Hernández
2015 Research in Computing Science  
Recommender systems are a tool to help student find information quickly and recommend new items of interest to the active student based on their preferences.  ...  The location of useful educational resources to support the learning process is addressed by using recommender systems.  ...  system framework for e-learning based on implicit and explicit collaborative filtering and sequential.  ... 
doi:10.13053/rcs-106-1-2 fatcat:funsqzfqvvcdxh54mh45vi7hhe

Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders [article]

Enmao Diao, Vahid Tarokh, Jie Ding
2022 arXiv   pre-print
Moreover, AAE has a broad application scope since it allows explicit or implicit feedback, user- or item-based alignment, and with or without side information.  ...  Multi-Target Multi-Domain Recommender Systems (MTMDR) aim to improve the recommendation performance in multiple domains simultaneously.  ...  Specifically, collaborative filtering learns from user-item interactions, while content-based recommendation is primarily based on side information.  ... 
arXiv:2110.13340v2 fatcat:2b5bzhl7n5br5m7zw3mk2xlfci

Matching Recommendation Technologies and Domains [chapter]

Robin Burke, Maryam Ramezani
2010 Recommender Systems Handbook  
The chapter aims to assist researchers and developers identify the recommendation technology that are most likely to be applicable to different domains of recommendation.  ...  Recommender systems form an extremely diverse body of technologies and approaches.  ...  and Collaboration at the Intelligent User Interfaces conference 2008.  ... 
doi:10.1007/978-0-387-85820-3_11 fatcat:eygp3krdtvh3da4xkz33meibce

Panorama of Recommender Systems to Support Learning [chapter]

Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis
2015 Recommender Systems Handbook  
This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000-2014).  ...  All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process.  ...  Ontology-based multi-actor learning flows and competence driven user models as described in[RS31-2010] can provide advice on tasks and resources [69] .  ... 
doi:10.1007/978-1-4899-7637-6_12 fatcat:fdi74mokhnhuhfqijgwmucgbny

Identifying Good Practices in Information Literacy Education; Creating a Multi-lingual, Multi-cultural MOOC [chapter]

Lyn Robinson, David Bawden
2018 Communications in Computer and Information Science  
frameworks; teaching and learning methods; interaction and collaboration by learners; structuring of learning materials; assessment methods; multi-lingual and multi-cultural aspects; IL outside higher  ...  Based on an analysis of published literature and reports, analysis of existing IL MOOCs, and expert opinion, it presents good practice eleven area: IL definition and models; IL content and contexts; pedagogical  ...  For example, the ANCIL model emphasises the value of collaborative learning, based on real needs, wherever possible [20].  ... 
doi:10.1007/978-3-319-74334-9_73 fatcat:ph2uafgwsnbzrlnwphfl27k54m

Dynamic Intention-Aware Recommendation System [article]

Shuai Zhang, Lina Yao
2017 arXiv   pre-print
by leveraging rich online and offline user data; (2) it embraces the benefits of embedding heterogeneous source information and shared representations of multiple domains to provide accurate and effective  ...  Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recommender systems.  ...  In our work, we intend to employ some open-source knowledge base, such as, YAGO, DBpedia, and domain ontology for reasoning and further improving the multi-domain recommendation result.  ... 
arXiv:1703.03112v2 fatcat:af3jq4emcna6heoyujwxwqdmgu

A Composite of Heterogeneous Sources Recommenders (CHR)

Md Masum, Jingyu Sun, Dong Wei
2020 International Journal of Computer Applications  
The first one is, at a specific point in time, what source of recommendation is a user likely to be responsive. And the other one is the optimal recommendation from an individual source.  ...  In this paper, we endeavor to generate a competent framework merging various heterogeneous item relationships by concurrently modeling based on two important questions.  ...  Collaborative Metric Learning (CML) [12]: Collaborative filtering is a classic method that learns metric embeddings for users and items.  ... 
doi:10.5120/ijca2020919915 fatcat:ogtu4xxhijgabm2d53jdbzomra

A Hybrid Recommender System for Recommending Smartphones to Prospective Customers [article]

Pratik K. Biswas, Songlin Liu
2021 arXiv   pre-print
In this paper, we propose a hybrid recommender system, which combines Alternative Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome  ...  Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust.  ...  RELATED WORK Related research can be grouped into three broad categories: 1) conventional recommender systems, 2) hybrid recommender systems, 3) deep learning based recommender systems.  ... 
arXiv:2105.12876v1 fatcat:6q3wpnmkerd7pnnpsfhvbilxca

Play in the Museum

Jonathan P. Rowe, Eleni V. Lobene, Bradford W. Mott, James C. Lester
2017 International Journal of Gaming and Computer-Mediated Simulations  
Digital games have been found to yield effective and engaging learning experiences across a broad range of subjects. Much of this research has been conducted in laboratory and K-12 classrooms.  ...  However, designing game-based learning environments presents several challenges.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.4018/ijgcms.2017070104 fatcat:dqmm4jjgzzehpeoze3s677i33a

APSF Special Interest Group: Perioperative Multi-Center Handoff Collaborative

Gina Whitney, Philip E. Greilich
2021 ASA Monitor  
Greilich, MD, MSc T he Perioperative Multi-Center Handoff Collaborative (MHC) is a national learning collaborative whose primary objective is to create pragmatic, scalable and sustainable solutions that  ...  the evidence-to-practice gap that currently exists.  APSF Special Interest Group: Perioperative Multi-Center Handoff Collaborative Gina Whitney, MD Philip E.  ... 
doi:10.1097/01.asm.0000725932.88781.6c fatcat:osjymlhyp5fnfh4rk764sbl4wa

JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation [article]

Zhiwei Liu, Lei Zheng, Jiawei Zhang, Jiayu Han, Philip S. Yu
2019 arXiv   pre-print
JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module.  ...  Cross-domain recommendation can alleviate the data sparsity problem in recommender systems.  ...  Cross-domain recommendation and broad learning Broad Learning [7] is a way to transfer the information from different domains, which focuses on fusing and mining multiple information sources of large  ... 
arXiv:1910.08219v1 fatcat:3wyulfy6ffexlo6zac3o2gbetm
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