An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score

Massaro, Alessandro; Giannone, Daniele; Birardi, Vitangelo; Galiano, Angelo

doi:10.3390/fi13060145

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…The study has used feedback-based Bayesian network in order to compute the level of expertise. Further, the work of Massaro et al [46] have used neural network along with LSTM in order to assess the influence of web-content over an experience of user. Social network plays a dominant role in its interactive web-content where SEO plays a significant challenge to promote information on such platform in presence of complicated connected nodes in social network.…”

Section: Review Of Seo Approaches Using MLmentioning

confidence: 99%

Insights into Search Engine Optimization using Natural Language Processing and Machine Learning

Vinutha¹,

Padma²

2023

IJACSA

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Among the potential tools in digital marketing, Search Engine Optimization (SEO) facilitates the use of appropriate data by providing appropriate results according to the search priority of the user. Various research-based approaches have been developed to improve the optimization performance of search engines over the past decade; however, it is still unclear what the strengths and weaknesses of these methods are. As a result of the increased proliferation of Machine Learning (ML) and Natural Language Processing (NLP) in complex content management, there is potential to achieve successful SEO results. Therefore, the purpose of this paper is to contribute towards performing an exhaustive study on the respective NLP and ML methodologies to explore their strengths and weaknesses. Additionally, the paper highlights distinct learning outcomes and a specific research gap intended to assist future research work with a guideline necessary for optimizing search engine performance.

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Section: Review Of Seo Approaches Using MLmentioning

confidence: 99%

Insights into Search Engine Optimization using Natural Language Processing and Machine Learning

Vinutha¹,

Padma²

2023

IJACSA

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“…Note that the size of the training data D should be large to obtain a good performance with the proposed keyword extractor in Equation (5). However, minor languages such as Korean usually do not have a large dataset labeled for keyword extraction.…”

Section: Sequence Labeling For Small Number Of Documentsmentioning

confidence: 99%

“…Keyword extraction is a task of identifying keywords in a lengthy document, and it is considered one of the essential tasks in data analysis, data mining, and information retrieval [1][2][3][4]. In the current environment, where countless documents are published through various media [5,6], it becomes time-consuming for humans to process all documents and identify their keywords. Consequently, the demand for automatic keyword extraction has increased [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

RoBERTa-Based Keyword Extraction from Small Number of Korean Documents

Kim,

Lee,

Park

et al. 2023

Electronics

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Keyword extraction is the task of identifying essential words in a lengthy document. This process is primarily executed through supervised keyword extraction. In instances where the dataset is limited in size, a classification-based approach is typically employed. Therefore, this paper introduces a novel keyword extractor based on a classification approach. The proposed keyword extractor comprises three key components: RoBERTa, a keyword estimator, and a decision rule. RoBERTa encodes an input document, the keyword estimator calculates the probability of each token in the document becoming a keyword, and the decision rule ultimately determines whether each token is a keyword based on these probabilities. However, training the proposed model with a small dataset presents two challenges. One problem is the case that all tokens in the documents are not a keyword, and the other problem is that a single word can be composed of keyword tokens and non-keyword tokens. Two novel heuristics are thus proposed to tackle these problems. To address these issues, two novel heuristics are proposed. These heuristics have been extensively tested through experiments, demonstrating that the proposed keyword extractor surpasses both the generation-based approach and the vanilla RoBERTa in environments with limited data. The efficacy of the heuristics is further validated through an ablation study. In summary, the proposed heuristics have proven to be effective in developing a supervised keyword extractor with a small dataset.

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“…A high school experiment course on the motion of charged particles in a magnetic field was created using the N OB O OK virtual experiment technology [27]. They created a thorough blueprint for the method of instruction in the classroom as well as a simulation of the sample problems.…”

Section: Related Workmentioning

confidence: 99%

Communication Analysis and Privacy in CAI Based on Data Mining and Federated Learning

Jiang

Wang

2023

Applied Sciences

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Due to the fact that client data do not need to leave the local area, a distributed machine learning framework can aggregate training from several clients while preserving data privacy. In this essay, the development of CAI both domestically and internationally is reviewed and summarized, and the current state of CAI is examined. Communication analysis has so far been a key academic and theoretical area in federated learning, and some theoretical contributions have become the crucial theoretical foundations for understanding, defending, and guiding various human social behaviors. The major objective of knowledge distillation based on model responses is to provide students the ability to rapidly replicate the teacher’s model’s output. The experimental results demonstrate that the optimized Smith Regan model adopts the “Smith Logan” teaching design model, selects the courseware structure and record preservation as the teaching content in the fundamental CAI courseware design, and optimizes the teaching design from the perspectives of learning environment analysis, learner characteristics analysis, etc. Based on this, the model’s accuracy and robustness are increased by 7.34%.

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An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score

Cited by 5 publications

References 39 publications

Insights into Search Engine Optimization using Natural Language Processing and Machine Learning

Insights into Search Engine Optimization using Natural Language Processing and Machine Learning

RoBERTa-Based Keyword Extraction from Small Number of Korean Documents

Communication Analysis and Privacy in CAI Based on Data Mining and Federated Learning

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