A personalized programming exercise recommendation algorithm based on knowledge structure tree

2021 ◽  
pp. 1-12
Author(s):  
Wei Zheng ◽  
Qing Du ◽  
Yongjian Fan ◽  
Lijuan Tan ◽  
Chuanlin Xia ◽  
...  
Keyword(s):  
Prediction Method ◽  
Knowledge Structure ◽  
Learning Objective ◽  
Cognitive Level ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Related Data ◽  
Structure Tree ◽  
Learning Programming ◽  
Relationship Of

Personalized exercise recommendation is an important research project in the field of online learning, which can explore students’ strengths and weaknesses and tailor exercises for them. However, programming exercises differs from other disciplines or types of exercises due to the comprehensive of the exercises and the specificity of program debugging. In order to assist students in learning programming, this paper proposes a programming exercise recommendation algorithm based on knowledge structure tree (KSTER). Firstly, the algorithm provides a calculation method for quantifying students’ cognitive level to obtain their knowledge needs through individual learning-related data. Secondly, a knowledge structure tree is constructed based on the association relationship of knowledge points, and a learning objective prediction method is proposed by combining the knowledge needs and the knowledge structure tree to represent and update the learning objective. Finally, KSTER imports a matching operator that calculates cognitive level and exercise difficulty based on learning objectives, and makes top-η recommendation for exercises. Experiments show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. The comparison experiments with real-world data demonstrate that KSTER effectively improves students’ learning efficiency.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform

2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Gao ◽  
Haiming Liang ◽  
Bingzhen Sun
Keyword(s):  
Online Shopping ◽  
Rapid Development ◽  
Dynamic Network ◽  
Topsis Method ◽  
Recommendation Algorithm ◽  
Time Periods ◽  
The Difference ◽  
Relationship Of ◽  
Hybrid Recommendation ◽  
Novel Model

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.

Literary work education model based on intelligent machine learning and reader scoring criteria factors

2021 ◽  
pp. 1-10
Author(s):  
Lei Han ◽  
Wei Li ◽  
Ming Zang
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Literary Work ◽  
Time Decay ◽  
Recommendation Algorithm ◽  
Intelligent Machine ◽  
Literary Education ◽  
Education Model ◽  
Work Education ◽  
Rating Criteria

In order to improve the effect of literary works education, this paper combines intelligent machine learning and reader scoring criteria factors to construct an intelligent education model, and proposes a collaborative filtering recommendation algorithm based on item proportion factors and time decay. When calculating the user similarity, this paper adds the scale factor of the intersection of common scoring items to all the scoring items, and considers the non-intersection part of the user scoring items. Secondly, when predicting the project score, this paper adds a time decay function, combines the forgetting curve law to modify the score prediction method, and combines the actual needs to construct the basic framework of the education model. In addition, this paper designs experiments to verify the performance of the literary work education model constructed in this paper. The research results show that the literary work education model constructed in this paper based on intelligent machine learning and reader rating criteria factors has a certain role in promoting the effect of literary education.

Study on Prediction of the Peasants’ Income Based on SVM

2014 ◽  
Vol 687-691 ◽  
pp. 5169-5172
Author(s):  
Li Na Zhang ◽  
Bo Yang
Keyword(s):  
Social Factors ◽  
Index System ◽  
Statistical Data ◽  
Influence Factors ◽  
Prediction Method ◽  
Svm Model ◽  
The Index System ◽  
Relationship Of ◽  
System Designs ◽  
Computing Speed

China is a big agricultural country, effective prediction of peasants’ income is very important. This study mainly uses the SVM theory to predict the peasants’ income. By analyzing the influence factors of peasants’ income, establishes the index system, that is corresponding relationship of peasants’ income and factors of social influence, According to this index system, designs the prediction method of peasants’ income based on SVM. Bases on the statistical data of social factors and peasants’ income between 1990-2012 in china, to train the SVM model, at the same time, the kernel function and parameters of SVM used were setting and compared. The experimental results show that the accuracy of RBF function is 90.7%, the time is 98ms, has higher accuracy and faster computing speed.

Abstract 12: Feasibility of Monitoring Covid-19 Related Stroke Patients Across a 14 Hospital Stroke System of Care

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Howard J Rho ◽  
Navdeep S Sangha
Keyword(s):  
Stroke Care ◽  
Stroke Patients ◽  
Specific Data ◽  
Related Data ◽  
Disease States ◽  
Hospital Systems ◽  
Pandemic Response ◽  
Relationship Of ◽  
The Relationship ◽  
Existing Data

Background: Identifying and tracking COVID-19 related data has been crucial to the pandemic response. Most hospital systems have created internal tracking databases specific to COVID-19 but separated from other disease specific data pools. Traditional methods for tracking and trending novel and specific data such as COVID-19 related strokes may require personnel with highly technical skills to abstract the data. We aimed to create a COVID-19 stroke dashboard which would easily auto-abstract and update data. Methods: A simple monitoring system was designed using PowerBI™ and Microsoft Suite™ products that model existing data sources without using other IT resources. Existing data queries from various sources were modeled into one report and the resulting data model was used to track and trend incidence of COVID-19 and its relationship to stroke care throughout a 14- hospital stroke system. Results: The report allowed region-wide identification and evaluation of several metrics, including: volume of code strokes, the volume of patients who had a stroke within two weeks before or after testing positive for COVID-19, the initial NIHSS, if alteplase was administered, reason for no alteplase administration, delay in alteplase administration and if related to COVID-19 and the relationship of COVID-19 cases to the volume of code strokes. It was found that the volume of code strokes significantly decreased during the time of the pandemic and was inversely related to the volume of COVID-19 positive cases being reported in a county. The tool also found that COVID-19 positive stroke patients increased as the overall COVID-19 hospital volume increased. Conclusion: Assessing the relationships between a novel disease and other disease states may lead to changes in hospital workflows and practices resulting into improved patient outcomes.

scholarly journals Relationship of Target-Based Orientation and Competitive Anxiety With Female Basketball Players’ Performance

2018 ◽  
Vol 6 (9a) ◽  
pp. 69
Author(s):  
Gul Eda Burmaoglu
Keyword(s):  
Significant Relationship ◽  
Statistical Technique ◽  
Sport Performance ◽  
Basketball Players ◽  
Competitive Anxiety ◽  
Related Data ◽  
Relationship Of

The main purpose of this research was to determine relationship of the target-based orientation and the Competitive anxiety with the young Basketball players’ performance in Erzurum province championship tournaments. So the whole young Basketball players’ of Erzurum Province participating in the championship tournaments in 2014 were selected as the statistical technique. The questionnaire of the target-based orientation at sport and the questionnaire of the competitive anxiety and the study of the Basketball players’' results were applied in order to gather the related data. This study was an applied and a descriptive-correlation type. The results showed there is a significant relationship between the target-based orientation and sport performance of Basketball players’ participated in Erzurum championship tournaments. There is also significant relationship between the task-based element and sport performance of the young Basketball players’. There is no observed a significant relationship between the target-based orientation and sport performance of the Basketball players’. There is a significant relationship between the competitive anxiety and the sport performance of the young Basketball players’ in the championship tournaments of Erzurum.

scholarly journals HYBRID RECOMMENDATION ALGORITHM BASED ON TWO ROLES OF SOCIAL TAGS

2012 ◽  
Vol 22 (07) ◽  
pp. 1250166 ◽  
Author(s):  
ZI-KE ZHANG ◽  
CHUANG LIU
Keyword(s):  
Hybrid Algorithm ◽  
Social Tagging ◽  
Great Success ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Data Set ◽  
The Past ◽  
Social Tags ◽  
New Family ◽  
Hybrid Recommendation

The past few years have witnessed the great success of a new family of paradigms, social tagging networks, which allows users to freely associate social tags to items and efficiently manage them. Thus it provides us a promising way to effectively find useful and interesting information. In this paper, we consider two typical roles of social tags: (i) an accessorial tool helping users organize items; (ii) a bridge that connects users and items. We then propose a hybrid algorithm to integrate the two different roles to obtain better recommendation performance. Experimental results on a real-world data set, Del.icio.us, shows that it can significantly enhance both the algorithmic accuracy and diversity.

scholarly journals A Location-Based Business Information Recommendation Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Shudong Liu ◽  
Xiangwu Meng
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Empirical Studies ◽  
Real World Data ◽  
Network Ties ◽  
Long Distance ◽  
Recommendation Algorithm ◽  
Business Information ◽  
Information Recommendation ◽  
Social Network Ties

Recently, many researches on information (e.g., POI, ADs) recommendation based on location have been done in both research and industry. In this paper, we firstly construct a region-based location graph (RLG), in which region node respectively connects with user node and business information node, and then we propose a location-based recommendation algorithm based on RLG, which can combine with user short-ranged mobility formed by daily activity and long-distance mobility formed by social network ties and sequentially can recommend local business information and long-distance business information to users. Moreover, it can combine user-based collaborative filtering with item-based collaborative filtering, and it can alleviate cold start problem which traditional recommender systems often suffer from. Empirical studies from large-scale real-world data from Yelp demonstrate that our method outperforms other methods on the aspect of recommendation accuracy.

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