scholarly journals Global and Local Tensor Factorization for Multi-criteria Recommender System

Patterns ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 100023
Author(s):  
Shuliang Wang ◽  
Jingting Yang ◽  
Zhengyu Chen ◽  
Hanning Yuan ◽  
Jing Geng ◽  
...  
Keyword(s):  
Recommender System ◽  
Tensor Factorization ◽  
Global And Local

Topic tensor factorization for recommender system

2016 ◽  
Vol 372 ◽  
pp. 276-293 ◽  
Author(s):  
Xiaolin Zheng ◽  
Weifeng Ding ◽  
Zhen Lin ◽  
Chaochao Chen
Keyword(s):  
Recommender System ◽  
Tensor Factorization

TBTF: an effective time-varying bias tensor factorization algorithm for recommender system

2021 ◽  
Author(s):  
Jianli Zhao ◽  
Shangcheng Yang ◽  
Huan Huo ◽  
Qiuxia Sun ◽  
Xijiao Geng
Keyword(s):  
Recommender System ◽  
Time Varying ◽  
Tensor Factorization ◽  
Effective Time ◽  
Factorization Algorithm

Factorization Techniques for Predicting Student Performance

2012 ◽  
pp. 129-153 ◽  
Author(s):  
Nguyen Thai-Nghe ◽  
Lucas Drumond ◽  
Tomáš Horváth ◽  
Artus Krohn-Grimberghe ◽  
Alexandros Nanopoulos ◽  
...  
Keyword(s):  
Student Performance ◽  
Recommender System ◽  
Matrix Factorization ◽  
Learning Objects ◽  
Tensor Factorization ◽  
Latent Factors ◽  
E Learning ◽  
Factorization Methods ◽  
Predicting Student Performance ◽  
Over Time

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to students. This chapter introduces state-of-the-art recommender system techniques which can be used not only for recommending objects like tasks/exercises to the students, but also for predicting student performance. We formulate the problem of predicting student performance as a recommender system problem and present matrix factorization methods, which are currently known as the most effective recommendation approaches, to implicitly take into account the prevailing latent factors (e.g. “slip” and “guess”) for predicting student performance. As a learner’s knowledge improves over time, too, we propose tensor factorization methods to take the temporal effect into account. Finally, some experimental results and discussions are provided to validate the proposed approach.

Sunspot Groups as Tracers of Sub-Surface Processes

2000 ◽  
Vol 179 ◽  
pp. 155-160
Author(s):  
M. H. Gokhale
Keyword(s):  
Magnetic Flux ◽  
Indian Institute ◽  
Surface Processes ◽  
The Sun ◽  
Global And Local ◽  
Sunspot Groups

AbstractData on sunspot groups have been quite useful for obtaining clues to several processes on global and local scales within the sun which lead to emergence of toroidal magnetic flux above the sun’s surface. I present here a report on such studies carried out at Indian Institute of Astrophysics during the last decade or so.

Establishing global and local coherence in discourse processing: Evidence from fMRI

2009 ◽  
Author(s):  
Paul van den Broek ◽  
Ben Seipel ◽  
Virginia Clinton ◽  
Edward J. O'Brien ◽  
Philip Burton ◽  
...  
Keyword(s):  
Discourse Processing ◽  
Local Coherence ◽  
Global And Local

Landmark use in pigeons: Determining direction by global and local landmarks

1991 ◽  
Author(s):  
Ken Cheng
Keyword(s):  
Global And Local

Coral husbandry for ocean futures: leveraging abiotic factors to increase survivorship, growth, and resilience in juvenile Montipora capitata

2021 ◽  
Vol 657 ◽  
pp. 123-133
Author(s):  
JR Hancock ◽  
AR Barrows ◽  
TC Roome ◽  
AS Huffmyer ◽  
SB Matsuda ◽  
...  
Keyword(s):  
Abiotic Factors ◽  
Larval Rearing ◽  
Abiotic Conditions ◽  
Montipora Capitata ◽  
Reef Restoration ◽  
Juvenile Corals ◽  
Bleaching Response ◽  
The Face ◽  
Survival And Growth ◽  
Global And Local

Reef restoration via direct outplanting of sexually propagated juvenile corals is a key strategy in preserving coral reef ecosystem function in the face of global and local stressors (e.g. ocean warming). To advance our capacity to scale and maximize the efficiency of restoration initiatives, we examined how abiotic conditions (i.e. larval rearing temperature, substrate condition, light intensity, and flow rate) interact to enhance post-settlement survival and growth of sexually propagated juvenile Montipora capitata. Larvae were reared at 3 temperatures (high: 28.9°C, ambient: 27.2°C, low: 24.5°C) for 72 h during larval development, and were subsequently settled on aragonite plugs conditioned in seawater (1 or 10 wk) and raised in different light and flow regimes. These juvenile corals underwent a natural bleaching event in Kāne‘ohe Bay, O‘ahu, Hawai‘i (USA), in summer 2019, allowing us to opportunistically measure bleaching response in addition to survivorship and growth. This study demonstrates how leveraging light and flow can increase the survivorship and growth of juvenile M. capitata. In contrast, larval preconditioning and substrate conditioning had little overall effect on survivorship, growth, or bleaching response. Importantly, there was no optimal combination of abiotic conditions that maximized survival and growth in addition to bleaching tolerances. This study highlights the ability to tailor sexual reproduction for specific restoration goals by addressing knowledge gaps and incorporating practices that could improve resilience in propagated stocks.

An Improved Hybrid Recommender System Using Multi-Based Clustering Method

2009 ◽  
Vol 129 (1) ◽  
pp. 125-132 ◽  
Author(s):  
Sutheera Puntheeranurak ◽  
Hidekazu Tsuji
Keyword(s):  
Recommender System ◽  
Clustering Method ◽  
Hybrid Recommender System ◽  
Hybrid Recommender
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