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International Coordination of Multi-Messenger Transient Observations in the 2020s and Beyond: Kavli-IAU White Paper
[article]
2020
arXiv
pre-print
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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
2018
International Journal of Engineering & Technology
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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
RECOMMENDER SYSTEMS BASED ON DEEP LEARNING
2022
Zenodo
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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
2015
Innovations in Global Health Professions Education
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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
2015
Research in Computing Science
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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]
2022
arXiv
pre-print
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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]
2010
Recommender Systems Handbook
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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]
2015
Recommender Systems Handbook
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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]
2018
Communications in Computer and Information Science
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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]
2017
arXiv
pre-print
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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)
2020
International Journal of Computer Applications
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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]
2021
arXiv
pre-print
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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
2017
International Journal of Gaming and Computer-Mediated Simulations
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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
2021
ASA Monitor
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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]
2019
arXiv
pre-print
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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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