A Novel Hybrid Methodology of Measuring Sentence Similarity

Yoo, Yongmin; Heo, Tak-Sung; Park, Yeongjoon; Kim, Kyungsun

doi:10.3390/sym13081442

Cited by 12 publications

(5 citation statements)

References 25 publications

(62 reference statements)

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“…In addition, deep learning was applied in the field of semantic analysis, which plays the biggest role in creating chatbots. Reference [44] proposed a method to measure the similarity between two sentences using deep learning. In addition, deep learning is widely used in the recommendation field.…”

Section: Application Of Deep Learning In Various Fieldsmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

Self Cite

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With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer's churn probability and shopping propensity without the burden of marketing costs in the online shopping mall.

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Section: Application Of Deep Learning In Various Fieldsmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

Self Cite

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“…Nevertheless, it represents an indubitably fertile and stimulating research ground that should be enhanced since it permits the derivation of techniques that may significantly improve the robustness of algorithms, particularly when dealing with huge sets of training data that are potentially perturbed by random noise components, while also allowing hidden symmetries within data to be highlighted. The latter aspect is particularly interesting when dealing with intrinsically structured problems as, e.g., in the case of NLP tasks, see, e.g., [29,30].…”

Section: Conclusion and Further Directionsmentioning

confidence: 99%

From Optimal Control to Mean Field Optimal Transport via Stochastic Neural Networks

Di Persio,

Garbelli

2023

Symmetry

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In this paper, we derive a unified perspective for Optimal Transport (OT) and Mean Field Control (MFC) theories to analyse the learning process for Neural Network algorithms in a high-dimensional framework. We consider a Mean Field Neural Network in the context of MFC theory referring to the mean field formulation of OT theory that may allow the development of efficient algorithms in a high-dimensional framework while providing a powerful tool in the context of explainable Artificial Intelligence.

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“…N: number of paragraphs in document V i : vector represents paragraph i V j : vector represents paragraph j T: vector represents the title A: vector represents the abstract C: vector represents the conclusion Recently, deep learning methods that calculate semantic similarity between texts have appeared [40]. The disadvantage of these methods is that their complexity is high, as well as the need to provide data for training.…”

Section: Score Paragraphmentioning

confidence: 99%

Present and absent keyphrases extraction: an approach based on sentence embedding

Ajallouda

Zellou

2022

IJEECS

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The automatic keyphrases extraction (AKE) of a document is any expression by which we can learn its content without having to read it. Keyphrases are exploited in natural language processing (NLP) applications. These phrases are often mentioned in the document but there may be some keyphrases that are not mentioned. In the field of AKE, researchers have exploited many techniques, such as statistical calculation, deep learning algorithms, graph representation, and sentence embedding techniques. Approaches that exploit embedding techniques calculate the similarity between a document and a candidate keyphrase, where similar phrases to the document are considered as keyphrases. Representing the document by a single vector makes its performance poor, especially in long documents. This is in addition to the inability of these methods to generate absent keyphrases. In order to overcome these problems, our paper proposes an unsupervised approach to AKE, based on the universal sentence encoder (USE) to represent candidate keyphrases and parts of the document probably containing keyphrases. Our method also generates keyphrases not mentioned in the text. We compared the performance of the proposed approach with other methods based on embedding techniques, where the results showed the superiority of our approach especially in long documents.

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A Novel Hybrid Methodology of Measuring Sentence Similarity

Cited by 12 publications

References 25 publications

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

From Optimal Control to Mean Field Optimal Transport via Stochastic Neural Networks

Present and absent keyphrases extraction: an approach based on sentence embedding

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