Personalized News Recommendation Based on the Text and Image Integration

Yang, Kehua; Long, Shaosong; Zhang, Wei; Yao, Jiqing; Liu, Jing

doi:10.32604/cmc.2020.09907

Cited by 7 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Web classification [1] is the process of assigning websites to one or more classification labels. Web classification plays an important role in content recommendation [2][3][4] and contextual search [5][6][7][8]. According to different types of classification label, web classification can be divided into different classification problems such as topic classification [9,10] and genre classification [11,12].…”

Section: Introductionmentioning

confidence: 99%

OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical Multilabel Classification

Dong

Zhang

Yadav

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Web genre plays an important role in focused crawling, web link analysis, and contextual advertising. In this paper, web genre is defined as the functional purpose and the information type contained in the website. The intelligent classification of web genre can predict the content and functional type of website. However, there are several critical challenges to solve the web genre classification problem: lack of web genre classification dataset and efficient web genre classification mechanism. To improve web genre classification performance, we crawled Chinese websites of different web genres and converted crawled data into a hierarchical multilabel classification dataset. A website knowledge graph is constructed based on the relationship of website and meta tag features. Using entity features extracted from the knowledge graph, we propose an online web genre classification model based on hierarchical multilabel classification (OWGC-HMC) to mine the functional purpose of the corresponding website. Experimental results show that our OWGC-HMC model can mine hierarchical multilabel structure of web genre and outperform other web genre classification methods.

show abstract

Section: Introductionmentioning

confidence: 99%

OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical Multilabel Classification

Dong

Zhang

Yadav

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…For use in a variety of internet-of-things (IoT) applications, Singh et al [55] developed a model known as an extreme learning machine (ELM). Yang et al [59] analyzed both the text and the images and then used the adaptive tag (AT) algorithm to derive user-interested tags. The Text Image-CNN model proposed by Yang et al [60] gathers information that is both overt and covert from both the text and the images to identify instances of fake news.…”

Section: Multimodal Fake News Detectionmentioning

confidence: 99%

EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Nadeem

Ahmed

et al. 2022

Sustainability

View full text Add to dashboard Cite

Due to the exponential increase in internet and social media users, fake news travels rapidly, and no one is immune to its adverse effects. Various machine learning approaches have evaluated text and images to categorize false news over time, but they lack a comprehensive representation of relevant features. This paper presents an automated method for detecting fake news to counteract the spread of disinformation. The proposed multimodal EFND integrates contextual, social context, and visual data from news articles and social media to build a multimodal feature vector with a high level of information density. Using a multimodal factorized bilinear pooling, the gathered features are fused to improve their correlation and offer a more accurate shared representation. Finally, a Multilayer Perceptron is implemented over the shared representation for the classification of fake news. EFND is evaluated using a group of standard fake news datasets known as “FakeNewsNet”. EFND has outperformed the baseline and state-of-the-art machine learning and deep learning models. Furthermore, the results of ablation studies have demonstrated the efficacy of the proposed framework. For the PolitiFact and GossipCop datasets, the EFND has achieved an accuracy of 0.988% and 0.990%, respectively.

show abstract

“…In formula (7), the number of occurrences of the word i in document d is represented by TF i,d , its inverse document frequency is represented by IDF i , and the inverse document frequency is used to measure whether a word is a stop word and other words that have no effect on the meaning of the sentence. In theory, the more times a word appears in an article, the more important the word is, but there are too many stop words.…”

Section: Building a Personalized News Recommendation Modelmentioning

confidence: 99%

“…At present, in the process of developing e-commerce activities, the recommendation system under the condition of information overload will be applied, while there are few personalized recommendation systems for news. However, as news is an indispensable part of daily life, the update speed of network news is extremely fast, which leads to users' inability to accurately nd the required information in a large amount of information [7][8][9]. erefore, personalized recommendation for news is of great signi cance.…”

Section: Introductionmentioning

confidence: 99%

Personalized News Recommendation Algorithm for Event Network

Han

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to improve the level of personalized news recommendation efficiently and accurately, an event network-oriented personalized news recommendation algorithm is proposed. First, the event network is used to analyze, predict users’ interests and preferences, and actively push information content to meet users’ personalized needs, so as to build a personalized news recommendation model. Under the mobile Internet technology, combined with the characteristics of the Internet, through the position and title similarity of sentences in the document and other features, the combined features are formed to calculate the sentence weight. Finally, the sentences are extracted according to the weight ranking to generate the news summary, so as to realize the research on personalized news recommendation algorithm for event network. The experimental results show that the proposed algorithm has high recall and coverage, short time, good recommendation effect, and strong recommendation performance.

show abstract

Personalized News Recommendation Based on the Text and Image Integration

Cited by 7 publications

References 0 publications

OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical Multilabel Classification

OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical Multilabel Classification

EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Personalized News Recommendation Algorithm for Event Network

Contact Info

Product

Resources

About