scholarly journals Features of YouTube’s recommendation algorithms on the example of a channel about esports

Obraz ◽  
2021 ◽  
Vol 35 (1) ◽  
pp. 84-100
Author(s):  
Oleksandr Petryk
Keyword(s):  
Video Content ◽  
Effective Parameters ◽  
Recommendation Algorithm ◽  
Effective Strategies ◽  
Video Viewing ◽  
Recommendation Algorithms ◽  
Global Platform ◽  
Thumbnail Images ◽  
Statistical Indicators ◽  
Selection Of

The purpose of the study is to identify relevant effective strategies for interaction with the recommendation algorithm on the YouTube platform in the process of creating and distributing video content. The topic of the study is relevant because YouTube is an active global platform for the distribution of video content. YouTube is the most visited video viewing platform in Ukraine and one of the most visited websites in the world. Understanding the work of recommendation algorithms will allow you to adapt video content so that it reaches the widest possible target audience. The result of the study was the selection of the most effective parameters of videos on YouTube based on the analysis of statistical indicators on the PETR1K channel: relevance and novelty, attractiveness (clickability) of thumbnail images, frequency of uploads to the channel, viewers ‘reaction to previous videos, viewers’ returns, audience retention and more.

scholarly journals A social trust and preference segmentation-based matrix factorization recommendation algorithm

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Wei Peng ◽  
Baogui Xin
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
User Preference ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Commercial Activities ◽  
Trust Relationships ◽  
Recommendation Algorithms ◽  
The Difference

AbstractA recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

scholarly journals Effective Strategies on Learning Arabic for Children

ALSINATUNA ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 68
Author(s):  
SOKIP
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Learning Strategies ◽  
Research Method ◽  
Learning Strategy ◽  
Existing Problems ◽  
Effective Strategies ◽  
Library Research ◽  
Literary Sources ◽  
Selection Of

Arabic learning can take place at various ages of either children, adolescents or adults. Learning Arabic is something needed for especially Muslim because it is important in Muslim life. This paper will explain about the Arabic learning strategy for children. This is important to discuss because children need special strategies in learning for their ages. In collecting information, the writer uses library research method, which is done by comparing several literary sources, especially those that are the main source of discussion to discuss about the existing problems. Then, as the result, language learning is a help to determine how, and how well, learners learn a foreign language. There are many variations on learning strategy. Arabic learning strategies for children include playing, singing, telling stories, projects, demonstration, and conversation. By using the suitable and fun strategy, children can develop their potential well and effectively. The selection of learning strategies depends on children’s ages and characteristics.  

scholarly journals SIS dynamic network model based on product semantics from the perspective of infectious disease dynamics

2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao
Keyword(s):  
Network Model ◽  
Purchase Intention ◽  
Recommendation System ◽  
Optimal Algorithm ◽  
Dynamic Network ◽  
Distribution Model ◽  
Recommendation Algorithm ◽  
Model Based ◽  
Dynamic Network Model ◽  
Recommendation Algorithms

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.

scholarly journals Efficacy and potential of phage therapy against multidrug resistantShigellaspp.

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6225 ◽  
Author(s):  
Swee-Seong Tang ◽  
Sudhangshu Kumar Biswas ◽  
Wen Siang Tan ◽  
Ananda Kumar Saha ◽  
Bey-Fen Leo
Keyword(s):  
Phage Therapy ◽  
Public Awareness ◽  
Multidrug Resistant ◽  
Genetically Engineered ◽  
Rapid Expansion ◽  
Suitable Alternative ◽  
Effective Strategies ◽  
Conventional Antibiotics ◽  
Selection Of

Shigella-infected bacillary dysentery or commonly known as Shigellosis is a leading cause of morbidity and mortality worldwide. The gradual emergence of multidrug resistantShigellaspp. has triggered the search for alternatives to conventional antibiotics. Phage therapy could be one such suitable alternative, given its proven long term safety profile as well as the rapid expansion of phage therapy research. To be successful, phage therapy will need an adequate regulatory framework, effective strategies, the proper selection of appropriate phages, early solutions to overcome phage therapy limitations, the implementation of safety protocols, and finally improved public awareness. To achieve all these criteria and successfully apply phage therapy against multidrug resistant shigellosis, a comprehensive study is required. In fact, a variety of phage-based approaches and products including single phages, phage cocktails, mutated phages, genetically engineered phages, and combinations of phages with antibiotics have already been carried out to test the applications of phage therapy against multidrug resistantShigella.This review provides a broad survey of phage treatments from past to present, focusing on the history, applications, limitations and effective solutions related to, as well as the prospects for, the use of phage therapy against multidrug resistantShigellaspp. and other multidrug resistant bacterial pathogens.

A Video Recommendation Algorithm Based on Hyperlink-Graph Model

2017 ◽  
Vol 5 (3) ◽  
pp. 49-63
Author(s):  
Songtao Shang ◽  
Wenqian Shang ◽  
Minyong Shi ◽  
Shuchao Feng ◽  
Zhiguo Hong
Keyword(s):  
Graph Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Internet Users ◽  
Recommendation Algorithms ◽  
The Matrix ◽  
Personal Recommendation ◽  
Low Efficiency ◽  
Sports Videos ◽  
Video Recommendation

The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.

scholarly journals A Triangular Personalized Recommendation Algorithm for Improving Diversity

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Cai ◽  
Xiaowang Yang ◽  
Yusheng Huang ◽  
Hongjun Li ◽  
Qiang Sang
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
Online Databases ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy ◽  
Third Dimension ◽  
Triangular Area

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
Author(s):  
WEI ZENG ◽  
MING-SHENG SHANG ◽  
QIAN-MING ZHANG ◽  
LINYUAN LÜ ◽  
TAO ZHOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
High Diversity ◽  
Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

Modeling of para-Xylene Synthesis via Nonselective Toluene Disproportionation

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Seyyed M. Ghoreishi ◽  
Seyyed H. Madani
Keyword(s):  
Operating Costs ◽  
Heat Of Reaction ◽  
Optimum Conditions ◽  
Effective Parameters ◽  
Toluene Disproportionation ◽  
Numerical Predictions ◽  
Para Xylene ◽  
Reactor Temperature ◽  
Feed Temperature ◽  
Selection Of

The effective parameters on p-Xylene yield in the nonselective disproportionation reaction over unmodified ZSM-5 catalyst were investigated using the validated model developed in this study. The results of this study indicate that toluene conversion and benzene/xylene ratio is increased by increasing feed temperature and reactor residence time. Even though an adiabatic operation is applied, an almost isothermal reactor temperature profile is predicted by the model due to the low heat of reaction. At different feed operating temperatures, different maximum para-Xylene yields are reached at different weight hourly space velocities (WHSV)-1. Since the model numerical predictions demonstrate that the lower feed temperature requires larger reactor to obtain the maximum p-Xylene yield, therefore, selection of optimum conditions is a function of two independent variables, feed temperature, and (WHSV)-1, and should be selected in order to minimize the objective function of total manufacturing and operating costs.

The Application of Hybrid Recommendation Algorithm in Education Cloud

2014 ◽  
Vol 551 ◽  
pp. 670-674 ◽  
Author(s):  
Gai Zhen Yang
Keyword(s):  
Cloud Computing ◽  
Recommendation System ◽  
Research Work ◽  
Operating Efficiency ◽  
Cloud Platform ◽  
Recommendation Algorithm ◽  
Development Platform ◽  
Recommendation Algorithms ◽  
Technology Applications ◽  
Cloud Development

When we face large amounts of data, how can we find the most suitable educational resources quickly has become a pressing issue. In this paper, on the basic of comparative study on traditional recommendation algorithms, we use the cloud computing to solve the traditional collaborative filtering algorithms suffer from scalability issues, the proposed algorithm is applied to the combination of recommended teaching cloud platform program, the program according to different recommended by demand different recommendation strategies; open source project Hadoop as a cloud development platform of the algorithm; recommendation algorithm, algorithm on top of Hadoop to achieve improved operating efficiency is relatively high, ideal parallel performance, fully proved the cloud platform and recommended algorithm combining the advantages. The research work on the recommendation system and teaching cloud computing technology applications to provide a useful reference.

scholarly journals Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiushan Zhang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
User Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

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