scholarly journals Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Feifeng Huang
Keyword(s):  
Neural Network ◽  
Social Media ◽  
New Media ◽  
Recommendation System ◽  
Rapid Development ◽  
Mobile Internet ◽  
Data Set ◽  
Data Volume ◽  
Short Video ◽  
Specific Implementation

With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the main F of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.

scholarly journals eBook Service in Shanghai Library

2016 ◽  
Vol 1 (1) ◽  
pp. 66 ◽  
Author(s):  
Qing Zhou
Keyword(s):  
New Media ◽  
Rapid Development ◽  
Public Library ◽  
Mobile Internet ◽  
Digital Reading ◽  
Reading Habits ◽  
Digital Resources ◽  
Tablet Pcs ◽  
One Stop ◽  
Further Development

In the 21st century, with the rapid development of mobile Internet, people's reading habits have started shifting from the traditional paper-based media, to completely new media such as cell phones, eBook readers, tablet PCs and so on. "Shanghai Library’s Urban Digital Reading Service Platform" integrates all types of collections of digital resources to support eBook readers, tablet PCs, smart phones and other types ofmobiledevices. Thisplatformprovidesaconvenient,low-costandfriendlylearninginterfacetoonline users, providing one of the best reading experiences. This also creates a one-stop public library reading platform, and meets the reader’s need for reading on the go. By investigating and researching libraries’ experiences and requirements for digital reading platforms and its internationalization service, this paper will discuss its further development.

Big Data and Analytics

2020 ◽  
pp. 47-65
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Analytics ◽  
Data Warehousing ◽  
Big Data Analytics ◽  
Structured Data ◽  
Social Media Analytics ◽  
Data Set ◽  
Data Volume ◽  
Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

scholarly journals Media Leaders Mining Based on Android News Apps

2019 ◽  
Vol 2 (1) ◽  
pp. 4
Author(s):  
Sijia Wang ◽  
Miao Zhang
Keyword(s):  
New Media ◽  
Rapid Development ◽  
Experimental Studies ◽  
Mobile Internet ◽  
Major Influence ◽  
Data Sets ◽  
The Public ◽  
Spread Of Influence ◽  
Rapid Dissemination ◽  
Media Reports

<p align="justify">With the rapid development of the mobile Internet, the mobile news apps have become the most important way for the public to obtain news. As a new media carrier and communication platform,the mobile news apps can promote the rapid dissemination of information and the rapid spread of influence.  Some media have a major influence  on the direction of other media reports and the behavioral decisions of the public. These media can be regarded as media leaders. Media leaders are very important in the dissemination of news. By identifying media leaders, companies or governments can promote sales or guide public opinion separately. This article believes that media leaders mainly achieve their own influence by publishing news, so this article uses the news published by the mobile news apps as an entry point. This paper firstly solves the problem of data crawling in mobile news apps, and proposes a data crawling method based on reverse analysis, and obtains the data source. Then, reconstruct the reprinting path of the news, and carry out accurate traceability. Finally, cluster the news based on LDA, and propose an algorithm for mining media leaders from three aspects: influence, activity and preference. Experimental studies of data sets have shown that our algorithms can effectively identify media leaders.</p>

scholarly journals IMPACTS OF MEDIA LITERACY LEVELS ON HUMAN TRAFFICKING (TPPO) CONTENT

2021 ◽  
Vol 14 (2) ◽  
pp. 185
Author(s):  
Popi Andiyansari ◽  
Ade Irma Sukmawati
Keyword(s):  
High School ◽  
Social Media ◽  
New Media ◽  
Human Trafficking ◽  
Media Literacy ◽  
Quantitative Method ◽  
Daily Life ◽  
Rapid Development ◽  
Age Group ◽  
The Media

New media is rapidly evolving and has an impact on our daily life. The rapid development of applications and increasing number of social media users can lead the users in a vulnerable condition. Human trafficking, also known as TPPO (Tindak Pidana Perdagangan Orang) in Indonesia, is one of the threats that users encounter, it commonly happens to young users who do not have enough information about it, but they can obtain TPPO information through employment advertisements in the media. The goal of this study is to look at media literacy levels and the correlations between them and TPPO message comprehension in new media. This research used a descriptive quantitative method with a correlation approach, in which associations between variables were measured. The media literacy levels of respondents were measured by using a Likert scale with a range of 1-5. The aspects measured were age, school origin and ownership of social media. These aspects and the TPPO message understanding in new media were measured by a Pearson scale. This study found that the highest level of media literacy was in the age group of 15 years old from SMAN 1(Public Senior High School) Pakem and that the number of social media account ownership did not show a significant relationship with the literacy levels. The measurement on the relationships between the media literacy levels and the TPPO message understanding by using a Pearson scale obtained 0.606; these results indicated that the correlation between both variables was high.

scholarly journals Sentiment Analysis of Events on Social Web

2020 ◽  
Vol 9 (4) ◽  
pp. 1232-1238
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Extraction Process ◽  
Online Data ◽  
Promising Area ◽  
Data Volume ◽  
Application Data

These days, Data volume has experienced enormous increase in volume, giving new challenges in technology and application. Data production has been expected at the rate of 2.5 Exabyte (1Ex-abyte=1.000.000Terabytes) of data per day. The main sources of data are: sensors collect climate information, traffic and flight information, social media sites (Twitter and Facebook are popular examples), digital pictures and videos (YouTube users upload 72 hours of new video content per minute), etc. Out of them social media becomes the prominent representative for the data source of big data. Social big data comes from the combination of social media and big data. Here, the data is mostly unstructured or semi-structured. The classical approaches, techniques, tools and frameworks for management of data have become insufficient for processing this huge volume of data and not capable for providing efficient solution to handle the increased production of data. The major challenge in data mining of big data is, its inadequate approaches to analyze massive amount of online data (or data streams). Specially, the field of sentiment analysis and predictive analysis has become so much promising area to place an organization at doom or at boom by provide accurate decision at accurate time. The current paper provides an insight of machine learning algorithm both supervised and unsupervised method; and the traditional knowledge extraction process. The application field of sentiment analysis, the issues those are faced during data collection and cleaning. This study flourishes a complete picture of recommendation system based on the sentiment analysis of events. The key motivation of the paper is to incorporate the event sentiment analysis and give the feedback and recommendation and illustrate the ongoing researches in the field of sentiment analysis and its application.

scholarly journals Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set

2019 ◽  
Vol 1 (2) ◽  
pp. 160-175 ◽  
Author(s):  
Junru Lu ◽  
Le Chen ◽  
Kongming Meng ◽  
Fengyi Wang ◽  
Jun Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Neural Network Model ◽  
User Profile ◽  
User Profiling ◽  
Technology Evaluation ◽  
Data Set ◽  
Value Prediction ◽  
User Interests ◽  
Evaluation Scores

With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

Incorporating LDA with LSTM for followee recommendation on Twitter network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brahim Dib ◽  
Fahd Kalloubi ◽  
El Habib Nfaoui ◽  
Abdelhak Boulaalam
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Short Term Memory ◽  
Real Life ◽  
Network Graph ◽  
Data Set ◽  
Content Type ◽  
Modeling Strategy ◽  
Searching Method

Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

scholarly journals Application of an emotional classification model in e-commerce text based on an improved transformer model

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247984
Author(s):  
Xuyang Wang ◽  
Yixuan Tong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Text Classification ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Mobile Internet ◽  
Sentiment Classification ◽  
Classification Model ◽  
Test Model ◽  
Transformer Model

With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification.

scholarly journals Research on the Influence of New Media Technology on Internet Short Video Content Production under Artificial Intelligence Background

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiqin Lu ◽  
Inyong Nam
Keyword(s):  
Artificial Intelligence ◽  
New Media ◽  
Large Scale ◽  
Rapid Development ◽  
Smart Phone ◽  
Video Content ◽  
Video Content Analysis ◽  
Short Video ◽  
Popular Work ◽  
Basic Features

With the rapid development of the Internet and smart phone technology, a large number of short videos are shared through social platforms. Therefore, video content analysis is a very important and popular work in machine learning and artificial intelligence currently. However, it is very difficult to analyze all aspects of video content originally produced by large-scale users. How to screen out bad and illegal content from short videos published by a large number of users, select high-quality videos to share with other users, and improve the quality of video on the distribution platform of the entire user is a top priority. Based on this background, this paper focuses on optimizing video auditing to provide basic features for algorithm judgment, supporting original content and increasing the distribution of new content, and strengthening manual intervention combining algorithm recommendation with manual recommendation. Four major aspects of the artificial training algorithm model discuss the optimization effect of artificial intelligence on the algorithm in order to provide some guidance for the sustainable and healthy development of mobile short video.

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