Methods and Means of Web Content Personalization for Commercial Information Products Distribution

Demchuk, Andriy; Lytvyn, Vasyl; Vysotska, Victoria; Dilai, Marianna

doi:10.1007/978-3-030-26474-1_24

Cited by 18 publications

(4 citation statements)

References 17 publications

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“…Content adaptation depends on deciding what content is most suitable and related to each user before adapting it (Demchuk, Lytvyn, Vysotska, & Dilai, 2019). It includes many forms of objects such as news, images, articles, resources, products, media, or fine-grained differences in textual wordings.…”

Section: Personalized Contentmentioning

confidence: 99%

Increasing Student Engagement with Personalized Emails

Alwagdani¹,

Alomar²

2020

CIS

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Despite a large number of studies in personalizing e-learning systems, only a few sufficiently cover the impact of personalization on university emails. Either universities impose the use of university email or students are reluctant to use it. For instance, It was found that at King Abdulaziz University only 23% of students were using the university email to communicate regularly, unlike the faculty members, who showed an 80% commitment. However, this paper attempts to investigate the effect of applying different personalized systems on the university email and whether using adaptation and adaptability techniques in the university email will cover the huge gap between the university and its members. Specifically, frameworks were built to evaluate the efficiency, frequency of error occurrence, effectiveness, and user satisfaction in each experiment. We focused on testing the usability of personalized email against the existing university email and then evaluate which approach (adaptive, adaptable, or mixed-initiative) is more favorable to personalize the university email. These were conducted and evaluated using 40 subjects. Interestingly, results show that subjects with personalized emails were most efficient and satisfied as well as errors were reduced by 42%. Furthermore, significant differences were found between the three approaches (adaptive, adaptable, and mixed-initiative), and the adaptive approach was the most preferred by the respondents. A set of empirically derived guidelines was also discussed as a basis for developing a suitable university email structure.

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Section: Personalized Contentmentioning

confidence: 99%

Increasing Student Engagement with Personalized Emails

Alwagdani¹,

Alomar²

2020

CIS

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“…However, the main problem is to choose the best algorithm to develop recommendations for domain RS of specific e-business. We shall analyze common and popular recommendation algorithms such as CBF, CF, KBF, DF, and HF [70].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

Lytvyn

Vysotska

Shatskykh³

et al. 2019

EEJET

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“…With the simplification of access to different data, expanded data search, copy, and distribution capabilities in networks, the task of authorship attribution is becoming even more urgent [72]. Similarly, the issues related to authorship attribution are important in linguistic, historical and forensic studies [73]. The availability of electronic devices makes it possible to push back the authorship attribution with the involvement of a large number of experts, to accelerate and simplify this process through its automation [74].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution

Lytvyn

Vysotska

Budz

et al. 2019

EEJET

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Розглянуто особливостi застосування технологiй лiнгвостатистики для iдентифiкацiї стилiстики автора текстового контенту науково-технiчного профiлю. Квантитативний лiнгвiстичний аналiз тексту використовує переваги контент-монiторiнгу на основi методiв NLP для визначення та аналiзу множини стопових слiв, ключових слiв, стiйких словосполучень та дослiдження N-грам. Останнi використовують в методах лiнгвометрiї для визначення приналежностi аналiзованого тексту конкретному авторовi у вiдсотках. Розроблено квантитативний метод автоматичного визначення авторства текстового контенту на основi статистичного аналiзу розподiлу 3-грам. Запропоновано пiдхiд реалiзацiї визначення автора україномовного тексту науково-технiчного профiлю. Отримано експериментальнi результати запропонованого методу для визначення приналежностi аналiзованого тексту конкретному автору за наявностi еталонного авторського тексту. Застосування лiнгвостатистичного аналiзу 3-грам до множини статей дозволить сформувати пiдмножину подiбних за лiнгвiстичними характеристиками публiкацiй. Накладання на пiдмножину додаткових умов у виглядi проведення статистичних та квантитативних аналiзiв (множини ключових слiв, стiйких словосполучень, стилеметричного, лiгвометричного тощо) дозволить значно скоротити цю пiдмножину, уточнивши список ймовiрнiших авторських робiт. Для якiсного та ефективного аналiзу контенту при визначеннi ступеня авторства конкретному автору пропонуємо аналiзувати еталонного тексту та дослiджуваного в декiлька етапiв: лiнгвометричний аналiз коефiцiєнтiв рiзноманiття авторського мовлення, стилометричний аналiз, аналiз стiйких словосполучень, лiнгвостатистичний аналiз 3-грам. Для автоматизованого опрацювання тексту має велике значення не тiльки частота появи тiєї чи iншої категорiї, а взагалi присутнiсть в дослiджуваному текстi. Кiлькiсний пiдрахунок дозволяє зробити об'єктивнi висновки щодо спрямованостi матерiалiв за кiлькiстю уживань одиниць аналiзу в дослiджуваних текстах. Якiсний аналiз робить те саме, але внаслiдок дослiдження того, чи зустрiчається (i в якому контекстi) певна важлива оригiнальна категорiя взагалi Ключовi слова: NLP, контент, контент-монiторiнг, стоп-слова, контент-аналiз, статистийний лiнгвiстичний аналiз, квантитативна лiнгвiстика, статистична лiнгвiстика, лiнгвометрiя UDC 004.

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Methods and Means of Web Content Personalization for Commercial Information Products Distribution

Cited by 18 publications

References 17 publications

Increasing Student Engagement with Personalized Emails

Increasing Student Engagement with Personalized Emails

Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution

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