scholarly journals Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

2021 ◽  
Author(s):  
Junwei Zhang ◽  
Min Gao ◽  
Junliang Yu ◽  
Lei Guo ◽  
Jundong Li ◽  
...  
Keyword(s):  
Group Recommendation ◽  
Hypergraph Learning ◽  
Double Scale

HIGH PREVALENCE OF MALNUTRITON AND WORM INFECTION AMONG VAN KIEU AND PAKOH CHILDREN AGED 12-36 MONTHS IN DAKRONG DISTRICT, QUANG TRI PROVINCE

2012 ◽  
pp. 129-134
Author(s):  
Thi Lan Tran ◽  
Thi Huong Le ◽  
Xuan Ninh Nguyen
Keyword(s):  
Cross Sectional Study ◽  
Age Group ◽  
Infection Status ◽  
Sectional Study ◽  
Group Recommendation ◽  
Related Factors ◽  
Normal Children ◽  
Cross Sectional ◽  
High Prevalence ◽  
Very High

Objectives: Assess the nutritional status, worm infection status and some related factors among children aged 12-36 months of Dakrong district, Quang Tri province. Subject and method: A cross sectional study was carried out in 2010, in 680 children aged 12-36 months in 4 communes of Dakrong district, Quang Tri province. Results: The malnutrition rate was 55.0% for underweight, 66.5% for stunting and 16.2% for wasting. The prevalence of malnutrition increases by age group. The prevalence of worm infection was 31.6%, the highest prevalence was belong to Ascaris infection (24.6%), followed by Hookworm and Trichuris (6.5% and 6.2%, respectively). The prevalence of worm infection among children under two is very high (27.0%). The prevalence of worm infection was distributed quite equally between the malnutrition children group and normal children group. Recommendation: Early deworming forchildren from 12 months should be considered as important strategy against the malnutrition of children in Dakrong district, Quang Tri province

Social-Enhanced Attentive Group Recommendation

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Da Cao ◽  
Xiangnan He ◽  
Lianhai Miao ◽  
Guangyi Xiao ◽  
Hao Chen ◽  
...  
Keyword(s):  
Group Recommendation

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

PolSAR image classification using a semi-supervised classifier based on hypergraph learning

2014 ◽  
Vol 5 (4) ◽  
pp. 386-395 ◽  
Author(s):  
Binghui Wei ◽  
Jun Yu ◽  
Cheng Wang ◽  
Hongyi Wu ◽  
Jonathan Li
Keyword(s):  
Image Classification ◽  
Hypergraph Learning
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